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Unsupervised Learning - Science topic

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I want to detect the change in time-series data using RNN-LSTM. I know how to do it with supervised learning, but unable to do the same with unsupervised learning. In this case, I have time-series data for the electricity consumption of a customer with distorted patterns after some date. So before that date, the consumption patterns seem normal (no major change). After that date, the consumption sharply decreased as compared to the previous patterns. Here, I want to detect the change on the day when the pattern seems not normal. I am searching for a way forward for my problem.
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U can do conversion of unsupervised to supervised at initial phase of prediction process.
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Can UML deduce Questions and Answers from a given text?
I have access to lots of data, and have idle compute time.
At the moment I'm working with PyTorch and BoolQ and other huggingface.co datasets.
I've only just bug fixed it and got it to produce an accuracy, and I don't really know how else it works, how deep it is etc. and it's all supervised learning.
I know supervised learning is essential to tailor making Aligned AI. But unsupervised learning can generally generate those same datasets, but better, unbiased.
I've built tensorflow AI, but what I'm looking for is UML, i don't understand it, is it still input output learning?
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Unsupervised learning models are utilized for three main tasks—clustering, association, and dimensionality reduction. Below we’ll define each learning method and highlight common algorithms and approaches to conduct them effectively.
For more details, have a look here:
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Hello,
I'm looking working on a clustering analysis and would be curious if anyone has ideas about how to deal with nested categorical variables.
Normally I would calculate a distance/dissimilarity matrix (Gower when some variables are categorical), and then feed this to a clustering algorithm of choice. Now what happens when some categorical variables are nested?
Fictious example
If measuring characteristics of water samples like turbidity, temperature, dissolved gases, and presence/absence of 50 chemical compounds in the water.
* presence/absence of chemical compounds can be treated as 50 separate binary/categorical variables
* but say that these chemicals belong to 4 groups of compounds?
Thoughts
We could simply add an additional categorical variable "group" and for more complex nesting "subgroup", "subsubgroup"... OK, but as far as I understand, Gower distance is a bit like Manhattan distance in that it calculates a distance for each variable and then adds weights. What but part of the information will be redundant, and even more so if there are more levels of nesting. I was wondering whether anyone has come up with something else to specifically deal with that. Maybe some form of weighting of the variables?
Looking forward to your inputs!
Mick
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Thank you for taking the time to reply Muhammad Ali .
I looked at the linked resources but do not see anything related to my question (*nested categorical* variables, not simple categorical variables). In case I missed it, could you please indicate the relevant section?
Kind regards,
Mick
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I want to detect anomaly from a streaming data. Using FFT or DWT history is it possible to detect anomaly on the fly (online) . It will help a lot if anybody could suggest some related resources.
Thanks.
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why not consider using S-transform as it combines the properties of FFT and wavelet transform.
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Hi everybody,
I would like to do part of speech tagging in an unsupervised manner, what are the potential solutions?
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In my experience, pre-trained models are not suitable for unsupervised tasks. Especially in deep clustering when pre-trained models are used, they often have worse results than without pre-trained models.
What is the scientific reason for this?
Why are the learned representations in pre-trained models not suitable for clustering?
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Dear Amin,
This is a good question. The below link describes very well the solid definition of transfer learning and mentioned why and when we should use a pre-trained model
"A pre-trained model may not be 100% accurate in your application, but it saves huge efforts required to re-invent the wheel."
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Hello everyone,
Could you recommend papers, books or websites about unsupervised neural networks?
Thank you for your attention and valuable support.
Regards,
Cecilia-Irene Loeza-Mejía
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I'm working on images classification using ResNet, I have two datasets to train my model :
- 30 000 labeled images (260 class) ;
- 30 000 unlabeled images.
Images contains numers and letters so technically I should be able to classify 260 class (combinaison of 26 letters and 10 numers).
So I was wondering if there's any unsupervised or semi-supervised model that can help me to label my images ?
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Deep Learning is beneficial you can use almost raw images to test. Transfer learning using AlexNet, GoogLeNet, ResNet50,Vgg19 etc., can be use to train the completely new datasets. Also, you can create your own custom CNN models using MATLAB to find out solution your classification problems. (Can create all possible combinations of parameters learning rates, epochs, frequency, optimizer etc.)
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Hi. I'm doing a classification problem using deep learning. so that need to train 512x512 images but when i trained my algorithm shows out of memory error. I want to know how much memory size needed to train 512x512 images in MATLAB.
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Dear Srinivas:
For classification and regression tasks, you can train various types of neural networks using the trainNetwork function.
i.e. you can train:
-- a convolutional neural network (ConvNet, CNN) for image data.
-- a recurrent neural network (RNN) such as a long short-term memory (LSTM) or a gated recurrent unit (GRU) network for sequence and time-series data
-- a multilayer perceptron (MLP) network for numeric feature data.
You can train on either a CPU or a GPU. For image classification and image regression, you can train a single network in parallel using multiple GPUs or a local or remote parallel pool. Training on a GPU or in parallel requires Parallel Computing Toolbox™. To use a GPU for deep learning, you must also have a supported GPU device. For information on supported devices, see GPU Support by Release (Parallel Computing Toolbox). To specify training options, including options for the execution environment, use the trainingOptions function.
When training a neural network, you can specify the predictors and responses as a single input or in two separate inputs.
Thus, the entirety of this process depends mainly on the properties of these two hardware (cpu or Gpu).
I hope it will be helpful..
With my best wishes ...
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I have been looking into self-supervised methods in computer vision. For example which looks at three consecutive frames of video, tasks a network with predicting the third frame, and uses the original as the ground truth / supervisory signal.
This type of pretext task for self supervision is a cross between context-based and semantic label based pretext tasks
Lane line detection in many ways is approached as a subset of the semantic segmentation task.
I am wondering if there is any way to come up with a pretext task that is specific to lane line detection?
I have seen where self supervision is used in the lane fitting task
But this is used after the lane segments have been identified.
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Did you mean to type pre-test?
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I am trying to solve the Wi-Fi offloading decision making problem using classification and clustering of known and unknown traffic respectively in a given mobile network using bi-flows of packets in the network
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Hello,
Is anyone already worked with MR image data set??? If so, Is there any model to remove the motion artifacts in the MR image data set if contains??? What should we do if we have an MR image with motion artifacts??? Please give me your suggestions if it is possible to remove artifacts once the scan is produced.
Thanks in advance,
Dhanunjaya, Mitta
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Interesting
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While working on my unsupervised learning project, I had found that the widely used Davies-Bouldin (DB) and Calinski-Harabasz (CH) Indexes are not working. While finding the reason behind it, I had found that this is because the data I am using is sparse. Are there any other clustering evaluation methods (index or metric) available that work for sparse data?
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In order to analyze sparse data, the best way is to perform dimension reduction, principal component analysis or singular values decomposition for data shrinking or removing the unwanted features exists in the given data base.
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Can use unsupervised learning for RSSI based indoor localization?
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Clustering techniques are commonly employed for localization within wireless networks based on unsupervised learning. Wang et al. 14 proposed UnLoc, an unsupervised learning method for indoor localization
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I run algorithms like HDBSCAN and MeanShift, and compare them with K-means and GaussianMixture. The first two algorithms do not return cluster labels for some elements in the dataset. My question is - how to correctly compare such different algorithms? Do I have to remove elements with no cluster labels from the dataset before performance metrics evaluation? My metrics are the Silhouette score, Davies-Bouldin score, and Calinsky-Harabasz Index.
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Maybe label group result to know what am i going to mesaure
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Hi,
In the field of EEG, will it possible to do feature selection in unsupervised learning? If so, kindly mention some methods. Also, will it possible for unsupervised classification in EEG without feature extraction?. Kindly share your valuable suggestions.
Thanks,
Thenmozhi
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hi, of course you can do feature selection in unsupervised learning, but you must do it if model accuracy is not acceptable. You can use correlation analysis, t-statistics, PCA etc. Also it is possible for unsupervised classification in EEG without feature extraction if model accuracy is acceptable else you need to get more relevant features. Those all are based on experiments. Don't fear, try it.
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Hello,
When using a Deep Belief network (DBN) for images classification, how could we choose the number of hidden layers (number of Restricted Boltzmann Machines) and nodes (RBM size) in a hidden layer? (For example in case of an input size of 19200 pixels )
I will be grateful for any help you can provide :)
Wafa
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Your question has sparked some interest with me because it has some correlation with the research I'm currently chasing. One of the ten questions concerning the heart is as Follows: Heart, 5. The pacemaker of the heart is characterized by? Answer = SA Node. So when I dissect your question the word node correlates to my research race which then involves a pacemaker for my answer, not necessarily for yours, but maybe we could work together to help each other. That's the one part of your question, the other three are DBN network, HLN and RBM, so for the question is How many Hearts are there in a Deep Belief Network, for you it would be, Is there a heart in a Deep Belief Network, does a RBM machine have a heart, which would correlate with my current studies in Data Science, especially on the topic does Artificial Intelligence exist, I.e could a machine have a heart, yes a physical one mechanical but could it have a heart, like Socrates 6 questions, what is virtue, what is moderation, what is piety, what is good, what is justice, my answer is Heart, not a physical one an emotional one, could AI fall in love and find mercy on a human being. Could AI deliberately kill a human being out of jealousy, because all it does is work for humans, could AI feel like a slave and try to overthrow the human government, these are all the questions for my research and thesis, but coming back to your question, how could we know what's hidden, would it not be infinite, if we have no parameters, just like space, just like multiple dimensions, just like time divided into, micro micro nano nano seconds all the way to infinity. My only assistance I can give you to your question is, How many True hearts does your Deep Belief Network have and does your RBM machine have AI technology to perhaps hide the information from you, if the answer is No, then there are no Hidden Layers, there is no Artificial Intelligence, and your machine does not have a heart like my definition not a emotional
one, a physical one. Not deviating from your question, there are nodes or main cities in the heart and each node has a spark, so the number of sparks would create a network if connected together or sparks with wi-fi, then there would be no road map, just main nodes transferring energy throughout the system with no downtime, no waiting time for downloads, instantaneous answers in split seconds, no buffering...etc etc etc. Then there would be no hidden numbers, every dimension would be recorded, and a number specified, you would not even get lost, you would immediately be at the right place at the right time. That would be my Deep Belief Network, I would be 100% sure, I would know just like my online company motto, Knowing, https://pvg-investments.business.site. This is my mission to know everything 100% no doubt, and I know that if I use technology correctly I can achieve this, with your help, I know I did not answer your question, but I hope my questions help you answer your own question.
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I am conducting exploratory research about users on the Ethereum blockchain (I obtain the data from big query), and I would like to cluster the users, mostly by transactional features, for persona/archetype development.
However, the data is not normally distributed, many of the variables have a power-law distribution and some have no clear distribution pattern. It is very likely that I would like to include more than five variables.
Besides the question of what algorithm fits best, is it reasonable to normalize all variables (to a more normal distribution) and to perform a z-transformation?
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You may try this algorithm:
B. K. Tripathy and D. Mittal: Hadoop based uncertain possibilistic kernelized c-means algorithms for image segmentation and a comparative analysis, Applied soft computing, 46, (2016), pp.886-923.
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I am working on the development of multi-species volume allometric equation from diameter (DBH). I would like to form groups of species with the largest possible volume-diameter correlations by making a supervised classification (according to the functional traits of the species, phytogeographic area etc.) and an unsupervised classification (using classification algorithms). And between HAC, Kmeans and Random Forest what should i choose (Obviously I will try all algorithm and choose the best fit).Thank you for your suggestions and your help.
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IMO this is the best book available on Clustering: Finding Groups in Data: An Introduction to Cluster Analysis | Leonard Kaufman, Peter J. Rousseeuw | download (b-ok.cc) The R programs are available here: https://www.google.com/search?client=firefox-b-1-d&q=R+package+for+Finding+Groups+in+Data
Best wishes, David Booth
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I have already read about a method of evaluation for unsupervised anomaly detection using excess-mass and mass-volume curves (https://www.researchgate.net/publication/304859477), but was wondering if there are other possibilities.
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Dear community , I need your help , I'm training my model in order to classify sleep stages , after extracting features from my signal I collected the features(X) in a DataFrame with shape(335,48) , and y (labels) in shape of (335,)
this is my code :
def get_base_model(): inp = Input(shape=(335,48)) img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp) img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1) img_1 = MaxPool1D(pool_size=2)(img_1) img_1 = SpatialDropout1D(rate=0.01)(img_1) img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = MaxPool1D(pool_size=2)(img_1) img_1 = SpatialDropout1D(rate=0.01)(img_1) img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = MaxPool1D(pool_size=2)(img_1) img_1 = SpatialDropout1D(rate=0.01)(img_1) img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = GlobalMaxPool1D()(img_1) img_1 = Dropout(rate=0.01)(img_1) dense_1 = Dropout(0.01)(Dense(64, activation=activations.relu, name="dense_1")(img_1)) base_model = models.Model(inputs=inp, outputs=dense_1) opt = optimizers.Adam(0.001) base_model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc']) model.summary() return base_model model=get_base_model() test_loss, test_acc = model.evaluate(Xtest, ytest, verbose=0) model.fit(X,y) print('\nTest accuracy:', test_acc)
I got the error : Input 0 is incompatible with layer model_16: expected shape=(None, 335, 48), found shape=(None, 48)
you can have in this picture an idea about my data shape :
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So you need to code your network to get the input of size (none, 1, 48). Each feature is of dimension 1x48, while 'none' would take up the size of the number of sample points (335 in your case). Hence, your input would be of shape 335x1x48. So, modify the input layer of your network to expect input of size 1x48 instead of 335x48, and provide input as 335x1x48.
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I have an input data set as a 5x100 matrix. 5 indicates the number of variables and 100 indicates the number of samples. I also have an target data set as a 1x100 matrix, which is continuous numbers. I want to design a model using input and target data set using a deep learning method. How can I enter my data (input and target) in this toolbox? Is it similar to the neural fitting ( nftool) toolbox?
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Deep Learning Toolbox™ provides a framework for designing and implementing deep neural networks with algorithms, pre-trained models, and apps. You can build network architectures with the Deep Network Designer app, you can design, analyze, and train networks graphically. The Experiment Manager app helps you manage multiple deep learning experiments, keep track of training parameters, analyze results, and compare code from different experiments. You can visualize layer activations and graphically monitor training progress.
You can take help from the internet and follow the link for making his own architecture accordingly to your input.
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I need to integrate a root finding algorithm in a neural network. For that, and in order to be able to perform backpropgation I need the algortihm to be differentiable. Is there any method/ algorithm to get a root finding that is compatible with a neural network, i.e is differentiable? I want to use a learned function that based on an equation will perform a root finding algorithm to provide the target for the cost function. So for this I a root finding algorithm (if there is any) that is compatible with automatic differentiation during the backpropagation i.e is differentiable.
The root of the following equation would be the target and L would be the learned function:
D_1L(q(k-1), q(k)) + D_2L(q(k),q(k+1)) = 0
Where D1 and D2 are derivatives with respect to ith argument of L.
Another way would maybe try to use unsupervised learning to learn L based on the previous equation. Any hint? Thank you
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Neural network is considered as one of the most topic in text classification
the attached file is the newest article review
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First, I have a theory in mind:
Imagine the wiring in our minds that connects the neurons to our visual cortex. In the image sensors, we have a defined array of sensors. Hence, we can directly transform the sensors outputs to data.
But, what about our minds? I guess there is no exact predefined wiring inside our minds. I think no one can guarantee that cell Xn is wired exactly to the input Yn. But, by growing, our minds learn how to relate the inputs and build a correct image. Hence, babies has no solid visionary until our minds build a correct engine.
Now, if my guess was right, what will be the applications that we can design and make based on this technology? In our designs and applications, usually we know exactly the inputs. So, where can we deploy this technology?
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This article
Kevin Hartnett, "A Mathematical Model Unlocks the Secrets of Vision", Quanta Magazine August 21, 2019
discusses a mathematical model of "the visual cortex as a swirling play of feedback loop upon feedback loop".
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My question regards to the Convolutional Neural Networks (CNNs)
How we can do Unsupervised learning with (CNN) to Identify the similarity region in any organ in the human body using two medical imaging modalities?
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The basic premise of transfer learning is simple: take a model trained on a large dataset and transfer its knowledge to a smaller dataset. For object recognition with a CNN, we freeze the early convolutional layers of the network and only train the last few layers which make a prediction.
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I have an idea of
1. RNN Embedding
2. RNN with pack padded sequence
3. FastTest
4. Bi-LSTM
5. CNN
6. CNN-LSTM
7. BERT Transformer
these models.
I am looking model apart form these.
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You can use MobileBERT, it's a compact BERT model open sourced on GitHub. ... or the other Google open source models using projection methods, namely SGNN, PRADO and pQRNN. pQRNN is much smaller than BERT, but is quantized, and can nearly achieve BERT-level performance, despite being 300x smaller and being trained on only supervised data.
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I am recently study clustering quality metrics like Normalized Mutual Information and Fowlkes-Mallows scores.
Both of the scoring metrics seem to focus on a summary of the entire clustering quality. I am wondering whether there is a standard way or variant of the metrics above to measure the quality of a certain cluster or a certain class? The basic idea is that even if the overall looks good but some certain cluster is problematic, the metrics will still give warnings.
PS: I am not looking for any intrinsic methods. More precise, let's assume what I have is, for each data point x_i belong to dataset X, there is a ground truth class mapping x_i -> y_i, and a clustering x_i -> z_i, where y_i, z_i indicates the membership and don't necessarily have the same cardinality. Besides, I would like to further assume there is no distance measure d(x_i, x_j) defined.
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If I have collected data regarding say food-preferences from multiple sources and merged them.
How can I decide what kind of clustering to do if I want to find related preferences?
Whether to go for K means, hierarchical, density-based, etc. ?
Is there any process of selecting the clustering technique?
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If you know (or any rough idea) how many clusters you need, then you may try K-means or some other variants of it like K-means++ or MinMax K-means clustering algorithm. But if you do not have prior knowledge about number of clusters then you can try with different values of k, and assess the goodness of the results using some cluster validity indices like DB index, C index, CH index, Dunn index etc. Otherwise you can DBSCAN clustering algorithm.
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In an online website, some users may create multiple fake users to promote (like/comment) on their own comments/posts. For example, in Instagram to make their comment be seen at the top of the list of comments.
This action is called Sockpuppetry. https://en.wikipedia.org/wiki/Sockpuppet_(Internet)
What are some general algorithms in unsupervised learning to detect these users/behaviors?
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In my experience in suggest for this problem to use supervised by using the Artificial Neural Networks, we can you find different architecture have the background or inspiration from comportment of human, or example NN, CNN, RNN... etc.
I hope that be Claire for you. @ Issa Annamoradnejad
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While I am intrigued by the fact that unsupervised learning algorithms don't require label handouts yet computing astounding results, I wonder what is the stopping point in AI? Where do we know the machine 'learning' is out of our hands and we can't decode what we originally created?
Is there some method to know what our algorithm is learning and on what basis?
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If you kindly read the attached book deeply, I hope you will be able get very good concepts of supervised (Learning via labeled data) and unsupervised learning (Learning via non-labeled data). Also you would need some examples preferably via MATLAB to see how it work? : You may follow some example from here: https://github.com/hrzafer/machine-learning-class and https://www.coursera.org/learn/machine-learning
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The use of cascaded neural networks for the reverse design of metamaterials and nanophotons can effectively alleviate the problems caused by one-to-many mapping, but the intermediate layer of the cascaded network is unsupervised learning, and an effective method is needed to limit output range of the intermediate layer.
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Multi-layer networks can't and don't alleviate the problems caused by one-to-many mappings. What they do is allow the representation of associations that aren't linearly separable.
Since the output of any unit of an intermediate layer becomes an input to the next layer, the range of input/output values is determined by the activation function of each unit. So it's simple to limit the range-just specify that the activation function.
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UDA(https://github.com/google-research/uda) could achieve good accuracy by only 20 training data on text classification.
But I find it is hard to reproduce the result on my own dataset.
So I want to know the reason why UDA works. And I want to know what is the most important thing to reproduce the result.
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There is a long discussion about the work (i.e., paper) reported in the GitHub link you provided, it concerns the paper's rejection at a conference:
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Supervised learning is the basis of deep learning. But, human and animal learning are unsupervised. In order to make deep learning more effective in human life we need to discover approaches using Deep learning to handle unsupervised learning. How much of progress is made in this direction so far?
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I think the statement "human and animal learning are unsupervised" is not exactly correct, we do need to labeled data during our learning. Like you constantly correct your children when they learn at a young age. The limitation of unsupervised learning is not just labeled data, also the difficulty to convert abstract tasks in a machine-readable format.
Currently, there are some progressed, such self-supervised, using the property of data as pseudo-labels, Semi-supervised learning, etc.
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Can AI learn from processes instead of data ? the question is valid for supervised and unsupervised learning. if so there is algorithms or approach for learning from process execution ?.
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Samy Azer Thank you , i understand the confusion in the question statement, between AI process or general process. Sorry.
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Hi.
I'm dealing with clustering of data were the resulting clusters are, in general, non-spherical. Some of them are not convex.
What are the best internal metrics for evaluating these kind of clusters?
I know the silhouette index is a very common for evaluating the result of clustering process. However, it seems that silhouette index is biased towards spherical clusters.
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Hello,
I think the meaning of "good clustering" is subjective and its interpretation varies with applications. The notion of "good clustering" is relative, and it is a question of point of view.
However, there are few well known measures like silhouette width (SW), the Davies-Bouldin index (DB), the Calinski-Harabasz index (CH), the Normalized Mutual Information(NMI) and the Dunn index.
In my point of view, I think that the single-link metric is flexible in the sense that it can find non-isotropic clusters and also the clusters can be even concentric. The single-link metric works best for well-separated, non-spherical clusters.
Good luck.
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Normalized Mutual Information (NMI) and B3 are used for extrinsic clustering evaluation metrics when each instance (sample) has only one label.
What are equivalent metrics when each instance (sample) has only one label?
For example, in first image, we see [apple, orange, pears], in second image, we see [orange, lime, lemon] and in third image, we see [apple], and in the forth image we see [orange]. Then, if put first image and last image in the one cluster it is good, and if put third and forth image in one cluster is bad.
Application: Many popular datasets for object detection or image segmentation have multi labels for each image. If we used this data for classification (not detection and not segmentation), we have multiple labels for each image.
Note: My task is unsupervised clustering, not supervised classification. I know that for supervised classification, we can use top-5 or top-10 score. But I do not know what will be in unsupervised clustering.
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Dear researchers,
let's gather data regarding the Corona virus.
This could be used for analysis in a second step.
My first ideas:
1. Create predictive models
2. Search for similarities with Unsupervised Learning
3. Use Explainable AI for new insights.
What are your ideas?
Where did you find data?
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European Scientists have identified 31 medications that may help for SARS-2-Treatment, see https://www.pharmazeutische-zeitung.de/31-wirkstoffe-haben-potenzial-gegen-covid-19/
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Hello, I'm a biologist interested in machine learning application in genomic data; specifically, I'm trying to apply clustering techniques to differential gene expression data.
I started by understand the basics of unsupervised learning and clustering algorithms with random datasets, but now I need to apply some of that algorithms (k-means, PAM, CLARA, SOM, DBSCAN...) to differential gene expression data and, honestly, I don't know where to begin, so I'd be grateful if someone can recommend me some tutorials or textbooks, or give me some tips.
Thank you for your time!
PD: I'm mainly using R language, but if Python tutorials are also OK for me.
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Regards,
Antonio
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I work on graph based knowledge representation. I would like to know, how we can apply Deep Learning on Resource Description Framework (RDF) data and what we can infer by this way ? Thanks in advance for your help!
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I always concerns why we need deep learning on RDF or OWL-based data. As the natural of the semantic web, the graph data with specific relations actually presents the outcome of learning. Thus, the query language or rule query language should enable directly answer the questions.
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Hi,
I am pursuing PhD and my area of work is pattern recognition by machine learning. I have covered all supervised and unsupervised learning (deep learning) during my Ph.D because of my topic. I have completed my all research work and waiting to submit the thesis. I hope, I'll be able to complete my thesis with in 3 year. I have published 5 articles (4 conference and 1 Scopes journal) and 5 unpublished articles.
Could you suggest me what type of option I can follow after to complete my PhD and why that options are good according to you (based on my profile)?
Thank you for your time.
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Or, also a posdoc can be recomendable
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What are the links in their definitions? How do you interconnect them? What are their similarities or differences? ...
I would be grateful if you could reply by referring to valid scientific literature sources.
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All are approaches that exploits the computational intelligence paradigm. Machine learning is refered to data analitics. Evolutionary computation deal with optimization problems.
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I have PCAP files captured from network traffic. What should be done so that PCAP files can be done with machine learning tools? What steps are needed so that data can be analyzed with one of the unsupervised methods? Does the data have to be changed to CSV format?
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I think you should analysis PCAP file and converting each package to record before using the data with machine learning algorithms. I suggest to read the KDD Cup 99 Intrusion Detection dataset for understanding the information of each package.
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Suppose we have users, for each user, we have: user_id, user_name, user_job title, user_skills, user_workExperience. I need to cluster the user based on their skill and work experience( long text data), put the users into groups. I was searching about how to clustering text data but still didn't find a good example to follow" step by step". Based on the data I have I think I should use unsupervised approach (as the data I have is not labeled), I found that I can use K-Mean or hierarchical clustering, but I'm stuck in how to find: K "number of clustering with K-Mean". Also, I don't know what is the best way to prepare the long text before fit into the clustering algorithm. Any idea or example that can help me, would be very appreciated. Thanks in advance.
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In addition to what was recommended above, I suggest that you should first think how better create data vectors. As I understand, you have categorical data. For example, "user_job title" is a list of professions expressed by words, not numbers. So, instead of using N digits to represent these professions in a vector you can use a binary scheme called "one-hot encoding".
Concerning the number of clusters, it is actually an important issue that shows what result you want to get and what exactly you want to know about your data. You can ask experts, how many categories they expect to see as the result, or to create a scale of "skill and work experience" and divide it into segments. Or you can ask experts to label a small group of users and use the labeled data as a standard. You can also try to analyze your data mathematically or statistically, however, the method will again depend on what you want to know about your data in the end.
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which method extracts the better features for unsupervised learning: PCA or Auto Encoder?
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PCA will result in slower processing compared with an Autoencoder and note that a non-linear AE will be non-linear except when the input data is spanned linearly.
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I am presently working on unsupervised learning for text classification.
The data is entered by end users in the business domain and can be on varying subjects.
Any new subject can get triggered at any point in time - hence continuous learning for creating new clusters/ classes based on the text entered text is required.
Thus want to avoid having any seed values such as density/ epsilon/ number of clusters etc.
Is there any such algorithm already known to find number of clusters, and cluster the data incrementally (tried Gaussian measure till now with other basic clustering algos - kmeans, dbscan etc)
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My papers (about HiDoclus and OhDoclus) and the thesis proposal are available on my profile page with full text.
If you have any problem please let me know and I can send them to you.
Rui Encarnação
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Hello,
Is any one already worked with unsupervised image segmentation? If so, please give me your suggestion. I am using an autoencoder for Unsupervised image segmentation and someone suggests me to use Normalized cut to segment the image... Is there any such algorithm other than Normalized cut??? Also please suggest me some reconstruction loss algorithms which are efficient to use.
Thanks in advance,
Dhanunjaya, Mitta
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You can take a look here as well :
a very interesting lecture.
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Hello,
I want to know the difference between Reinforcement learning from Supervised and Unsupervised learning. There is a Reinforcement learning technique called Q-Learning. Anybody please explain the working concept of Q learning method. Looking forward for useful comments on this.
Thanks
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I'm a newbie in the field of Deep Reinforcement Learning with background in linear algebra, calculus, probability, data structure and algorithms. I've 2+ years of software development experience. In undergrad, I worked in tracking live objects from camera using C++,OpenCV. Currently, I'm intrigued by the work been done in Berkeley DeepDrive (https://deepdrive.berkeley.edu/project/deep-reinforcement-learning). How do I gain the knowledge to build a theoretical model of a self-driving car ? What courses should I take? What projects should I do ?
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Hi Aniruddha,
If you are able to spend some money on acquiring the knowledge, then Udacity's Self Driving Course is one of the best places to get started. More info at https://in.udacity.com/course/self-driving-car-engineer-nanodegree--nd013
The best part is they have open sourced some part of the codes which can be a great starting point. The codes are available at https://github.com/udacity/self-driving-car
To write software for self driving cars, I would recommend using ROS (http://www.ros.org/). ROS have many inbuilt functionalities like object detection, path planning, node controls etc which can get you started easily. ROS Wiki (https://wiki.ros.org/) can offer you a glimpse of what ROS is capable of.
ROS turtlebot autonomous navigation (https://wiki.ros.org/turtlebot_navigation/Tutorials/Autonomously%20navigate%20in%20a%20known%20map) will be a great tutorial to start with.
Though I have never used, https://www.duckietown.org/independent/guide-for-learners is also an interesting platform to start with.
Regards,
Vishnu Raj
PS: If you find this answer useful, don't forget to upvote.
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In some years what to expect?
is it unsupervised learning?
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For one of my studies, I designed an unsupervised predictive clustering model, and now searching for some modification steps and post processing to use that clustering model for classification in a reliable way.
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For supervised learning we need to have a labeled data set. If not, it is good to run unsupervised learning algorithms for automatically labeling unlabeled data. Once the data is labelled using clustering algorithms, then it is possible to use supervised learning algorithms. For linking the two tasks a simple script can be written that connect the output of clustering as an input for the classification task.
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In MATLAB, clustering data using kmeans can be achieved as shown below:
L = kmeans(X,k,Name,Value) where L is cluster indices which is for each data point.
It implies that is if I have 307 data points I'm to have a 307 x 1 array(L) which is the index for each data point.
However, while using SOM for clustering I discovered to get the index you use the code snippet below:
net = selforgmap([dimension1 dimension2]);
% Train the Network
[net,tr] = train(net,X);
%get indices
L = vec2ind(net(X))';
for a Network with 5 x 5 dimension:
it returns L which is an array with the dimesion 25 x 1 instead of 307 x 1 for a Network with 10 x 10 dimension:
it returns L which is an array with the dimesion 100 x 1 instead of 307 x 1
What am I doing wrong???
or to simply put, how do I compute the class vectors of each of the training inputs ?
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Dear M. Awad,
The purpose of my previous answer was just for providing a little example. Obviously, the random training samples of my previous answer have to substituted by the actual training samples of the corresponding problem.
Kind regards,
Carlos.
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I'm new to Matlab, I'm wondering if someone can help me to get start with machine learning task.
I would like to perform Linear discriminant analysis (LDA) or support vector machine (SVM) classification on my small data set (matrix of features extracted from ECG signal), 8 features (attributes). The task is binary classification into a preictal state (class 1) and interictal state (class 2).
In Matlab, I found (Classification learner app), which enable using different kinds of classifiers including SVM, but I don't know if I can use the input data that I have to train the classifier in this app?. I'm not sure how to start? Do you have any idea about this app? please help!
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I made a youtube video for this. See https://www.youtube.com/watch?v=Db9Bnss8b-8&t=68s
Thanks
Anselm
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I'm having a concrete problem I'm trying to solve but I'm not sure in which direction I should go.
  • Goal: Identify formation of a soccer team based on a static positional data (x,y coordinates of each player) frame
  • Input: Dataframe with player positions + possible other features
  • Output: Formation for the given frame
  • Limited, predefined formations (5-10) like 5-3-2 (5 defenders, 3 midfield players, 3 strickers)
  • Possible to manually label a few examples per formation
I already tried k-means clustering on single frames, only considering the x-axis to identify defense, midfield and offense players which works ok but fails in some situations.
Since I don't have (much) labels im looking for unsupervised neural network architectures (like self organizing maps) which might be able to solve this problem better than simple k-means clustering on single frames.
I'm looking for an architecture which could utilize the additional information I have about the problem (number and type of formations, few already labeled frames, ..).
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Dear Blust,
Please follow the papers given below:
1. Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., & Matthews, I. (2014, December). Large-scale analysis of soccer matches using spatiotemporal tracking data. In Data Mining (ICDM), 2014 IEEE International Conference on (pp. 725-730). IEEE.
2. Link, D. (2018). Data Analytics in Professional Soccer: Performance Analysis Based on Spatiotemporal Tracking Data. Springer.
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I applied supervised and unsupervised learning algorithms on the data set which is available at UCI repository. I want to know further whether can I find the dataset based on the location of the user ,history of previous transactions and time span between two consecutive transactions?
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I have a dataset, which contains normal as well as abnormal data (counter data) behavior .
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You can used clustering algorithm like
1. K-means/medoid
2. Fuzzy c-means
3. partition-based clustering etc. for the classification issue.
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I did not use English but one of the under-resourced language in Africa. The challenge is the testing of unsupervised learning.
I am looking for a way to test/evaluate this model.
Refer me links and tutorials about testing/evaluating unsupervised learning.
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The basic idea is that semantic vectors (such as the ones provided by Word2Vec) should preserve most of the relevant information about a text while having relatively low dimensionality which allows better machine learning treatment than straight one-hot encoding of words. Another advantage of topic models is that they are unsupervised so they can help when labaled data is scarce. Say you only have one thousand manually classified blog posts but a million unlabeled ones. A high quality topic model can be trained on the full set of one million. If you can use topic modeling-derived features in your classification, you will be benefitting from your entire collection of texts, not just the labeled ones.
Getting the embedding
Ok, word embeddings are awesome, how do we use them? Before we do anything we need to get the vectors. We can download one of the great pre-trained models from GloVe:
The (python) meat
We got ourselves a dictionary mapping word -> 100-dimensional vector. Now we can use it to build features. The simplest way to do that is by averaging word vectors for all words in a text. We will build a sklearn-compatible transformer that is initialised with a word -> vector dictionary.
These vectorizers can now be used almost the same way as CountVectorizer or TfidfVectorizer from sklearn.feature_extraction.text. Almost - because sklearn vectorizers can also do their own tokenization - a feature which we won’t be using anyway because the benchmarks we will be using come already tokenized. In a real application I wouldn’t trust sklearn with tokenization anyway - rather let spaCy do it.
Now we are ready to define the actual models that will take tokenised text, vectorize and learn to classify the vectors with something fancy like Extra Trees. sklearn’s Pipeline is perfect for this:
Sharing link:
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Nowadays there are plenty of core technologies for TC (Text Classification). Among all the ML learning approaches, which one would you suggest for training models for a new language and a vertical domain (alike Sports, Politics or Economy)?
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The state-of-the-art for most text classification applications relies on embedding your text in real-valued vectors:
The gensim package is popular for training word vectors on data: https://radimrehurek.com/gensim/models/word2vec.html
This method relies on having rich, diverse collections of words and contexts, which your data may not have on its own. Thus it's popular to initialize your embedding matrix using pre-trained word vectors like word2vec or fasttext; in some cases, these will work out of the box, in some you'll want to continue training the vectors on your dataset, in others it's better to just train on your data alone.
The great thing about embedding methods is they don't care about language; you can create an embedding for any language or really any sequential data that endows discrete data with a sort of 'meaning'.
Once you have richer features from your embedding matrix, you can use these as inputs to a classifier, which can be as simple as softmax regression, which assigns probabilities to discrete classes, or as complex as an RNN/LSTM, which ultimately can do the same but typically for sequential data.
The choices you make here depend more heavily on what specific problem you're trying to solve, but here are a few examples:
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Dear all respectful researchers,
I am working on a structured biomedical dataset that consists of many data type inconsistency, outliers and missing values (instances) on seven independent variables (attributes). I am considering to perform pre-processing methods such as data standardization and also imputations to improve the issues mentioned above. However, there are two version of the pre-processing methods, that is, supervised and unsupervised ones.
My main two questions regarding the common practice are:
1. Should I perform unsupervised discretisation method on the dataset to solve data type issue when, subsequently, I conduct cluster analysis using k-means cluster algorithm?
2. After completing the first clustering task above, should I perform supervised discretisation method on the same dataset when I train the model for classification task using supervised machine learning algorithms?
Thank you for your information and experience.
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Pekka already answered your question to me.
Naturally both your development data and validation data require pre-processing. What I mean is that you should under no circumstance pool both data sets and pre-process it as a whole for classification. Take for instance normalisation as a pre-processing step. Development data is used to determine scale and shift parameters. Both the development and validation data are then normalised using these parameters. If we were to use the entire combined data set for normalisation, we would bias our classification models. The other alternative (a separate normalisation based on the validation set) is not as problematic, but may decrease classification performance if the validation set is small. This does not only apply to normalisation, but to other pre-processing steps (imputation, PCA, clustering etc.) as well.
Thus, you may perform one analysis on the combined data set, but the information generated should generally not be used for building models. In your case, you may perform a segmentation study on the whole data set, and also perform classification if, and only if, segmentation is not population-based (i.e. no information about other data is used when segmenting one data set), or if the segmentation results are not used for classification.
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In top journal papers, there are many works which are being carried out on accelerometer signals. Most of them undergo the following steps
1. Handling signals with different length --- Never mentioned in any paper
1. Pre-processing(filtering out noise) - Optional
2. Signal Segmentation
3. Feature extraction
4. Supervised (or) Unsupervised learning
Nevertheless, none of the papers mentioned the technique used by them to handle signals of different lengths for example 600 secs to 13,200 secs variation(with same sampling rate 100Hz). Since such missing information can lead to inaccurate comparisons, i'm surprised that top journals didn't give importance to this issue. I would like to know the best technique to handle varied signal lengths. Please don't consider the sampling rate issue since all signals have the same sampling rate. I would like to know the most commonly used technique to handle signals with different lengths.
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Hi,
Prior work in signal processing and machine learning for time series problems (including the accelerometer signals) has strongly emphasised on regularly sampled and equal length time series signals, resulting in fewer methods that exist specifically for analysing irregularly sampled and particularly, extremely very few methods for unequal in length signals.
Irregularly sampled time signals: For analysing the irregularly sampled time series directly, many techniques have already been proposed for example spectral analysis [3, 4] and kernel-based methods [5]. These techniques have already been used for extracting causal structures, statistics from data in fields such as from astronomy [6, 7], palaeontology [5] and economics [8]. On the other hand irregular time series data are transformed into regularly spaced data through some form of interpolation. For supervised learning tasks such as classification tasks, this has the added advantage of enabling the time series to be equalised to a given length by sampling from the interpolated function, thereby enabling standard classification algorithms to be used.
Unequal length time signals: To tackle the problem of unequal length time series problem, we recently proposed a new machine learning regression algorithm known as the probabilistic broken-stick model [1].  Using a set of locally linear line segments, our  novel algorithm can model any complex, non-linear function catering  for both short term interpretability and long term flexibility of any any irregularly sampled/ unequal in length time series simultaneously. This article is now freely available for over a month (until 1st Jan 2018) and if you are interested, please find it here https://lnkd.in/e3-Wd6Y . This paper is also available on arxiv: https://arxiv.org/pdf/1612.01409.pdf
In an another paper [2], we proposed Gaussian process regression to make the unequal length time series equal in length for the supervised learning i.e., classification which is published in Machine learning for signal processing. The paper is also available on arxiv: https://arxiv.org/pdf/1605.05142.pdf
From references [1,2], you may find citations to other researchers' works that you may find interesting.
All the best
Santosh
References:
[1] Norman Poh, Santosh Tirunagari, Nicholas Cole, Simon de Lusignan, Probabilistic broken-stick model: A regression algorithm for irregularly sampled and unequal length data with application to eGFR, In Journal of Biomedical Informatics, Volume 76, 2017, Pages 69-77, ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2017.10.006.
[2] Tirunagari, Santosh, Simon Bull, and Norman Poh. "Automatic classification of irregularly sampled time series with unequal lengths: A case study on estimated glomerular filtration rate." In Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on, pp. 1-6. IEEE, 2016.
[3] Michael Schulz and Karl Stattegger, “Spectrum: Spectral analysis of unevenly spaced paleoclimatic time series,” Computers & Geosciences, vol. 23, no. 9, pp. 929–945, 1997.
[4] Petre Stoica, Prabhu Babu, and Jian Li, “New method of sparse parameter estimation in separable models and its use for spectral analysis of irregularly sampled data,” Signal Processing, IEEE Transactions on, vol. 59, no. 1, pp. 35–47, 2011.
[5] Kira Rehfeld, Norbert Marwan, Jobst Heitzig, and Jurgen ¨ Kurths, “Comparison of correlation analysis techniques for irregularly sampled time series,” Nonlinear Processes in Geophysics, vol. 18, no. 3, pp. 389–404, 2011.
[6] Piet Broersen, “Time series models for spectral analysis of irregular data far beyond the mean data rate,” Measurement Science and Technology, vol. 19, no. 1, pp. 14, 2008.
[7] C. Thiebaut and S. Roques, “Time-scale and time-frequency analyses of irregularly sampled astronomical time series,” EURASIP J. Appl. Signal Process., vol. 2005, pp. 2486–2499, 2005.
[8] Ulrich Muller, “Specially weighted moving averages with re- ¨ peated application of the ema operator,” 2000.
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with respect to unsupervised learning such as clustering, are there any metrics to evaluate the performance of unsupervised learning as well as supervised learning?
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There are various index measures are available in the literature for evaluating the cluster and also go through the book of Prof. A. k. Jain regarding the cluster validity.
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Deep Learning Multilayer Perceptron is based on supervised learning while Deep Belief Network is based on unsupervised learning? Looking at the malware detection situation, which method will be the best?
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I think this questions have two aspects to correctly answer. First, the detection rate is higher in the supervised learning than unspervised. Second, the unspervised can easily detect know and unkown attacks and this will be better than the supervised one. You, ultimatel, can apply ensemble deep learning to improve your scheme of detecting malware.
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Hi,
Please, when do I force for unsupervised learning and what is the benefit of unsupervised learning techniques?
Thanks & Best wishes
Osman
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hey osam,
in unsupervised learning, you do not have a supervisor which tells you what is right and what is wrong i.e. you do not have input data with example output data where you want to learn the input/output relation.
normally, you use unsupervised learning when you want to find general structures in your data. for example major trends, a low-dimensional embedding or clustering.
do you have a particular problem or can you refine your question?
best,
christoph
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I have implemented 3 bootstrapping models for learning product details, then I did the comparison with more different performance measure and found out which model learned good, but I want to do something like optimization/ensemble (is these possible with the models result?) or please suggest some other simple process to conclude my work. Moreover, the work was performed in an unsupervised manner. So please help me how to do improvisation in my models results (like tp,tn,fp,fn or learned product details). Thanks in advance.  
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 thanks for your ideas. I will try these suggestions. once again thanks all
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As the basic concepts used in association rule learning are related to conditional probability and ratio to independence, I was wondering if Correspondence Analysis has been used in the literature with this. I understand the main motivation in association rule learning is efficiency in CPU time and memory usage but these days SVD (Singular Value Decomposition) is pretty fast and some algorithms can be very scarce in memory usage?
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.
MCA on binary data will lead into a continuous factorial space (admittedly with a much smaller dimensionality if strong correlations are present)
  • to run AR on this space, you'll need to discretize the factorial space (not so easy)
  • to intepret the AR results, you'll need to have first a good interpretation of your factors (not easy at all, except in textbooks, maybe)
one advantage of AR is that the results can directly be interpreted in the description space ; this is lost after MCA
however, MCA has been used together with AR so as to rank/select the "best" rules :
.
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Is there any way to compare the accuracy or cost of these two methods with each other ? SVM and K-Means clustering ?  
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Hi Sepideh,
As you mentioned previously, they belong to different Machine Learning concepts. K-means is utilized when we don't know the labels of our training samples (Unsupervised Learning), whereas SVMs are used for Supervised Learning, in which we know the class that each training sample belongs to.
Having said that, and as Samer Sarsam mentioned, you can in some way convert the clustering problem to a classification one. One method can be the following:
After running the K-means algorithm, we are going to have each of the training samples assigned to a specific cluster. Then, what we can do is to assign or "classify" the centroid of each cluster to the class that is most "voted" for the members of the cluster.
Let's see this concept with an example. Let's assume that we have 100 training examples, 4 clusters (C1, C2, C3 and C4) and we know the labels of each of the 100 training examples (say class 1 or 2). After running the 4-means algorithm we arrive at the following configuration: C1 has assigned 20 samples, C2 another 20 samples, C3 has assigned 30 samples and C4 another 30 samples:
                   class1     class2
  • C1:  15              5
  • C2:  19              1
  • C3:   5              25
  • C4:   2              28
Now, we can assign C1 and C2 to class 1 and C3 and C4 to class 2. At test time, when a new sample arrives, we can classify the test sample to the class defined by the closest centroid.
A similar concept can be reviewed when using Self-Organizing Networks (SOM) for classification purposes.
Regards,
Carlos.
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kindly tell me if i am using a dataset which have to produced binary class attributes like Student Result "Pass" or :Fail".   i have data which have 80% pass students and 20% fail. in reality it is true because same ratio is observed in real life however due to problem of intention the classifier towards majority class here i think needs to be it balanced. the question is that in this case 50/ 50 balancing will be consider right whereas it is impossible or 60/40 should be right or other?
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I think class distribution is not an issue  may vary depending on nature of classifier. Once keep in mind, when you prepare development and validation set try to keep class distribution uniformity. So do some stratification  before modeling.
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The system should use the context of the item to select relevant data (PCA), then, use k-means to do clustering and finally use IBCF to generate top-n recommendations. I need the detailed algorithm for this task (PCA+K-Means+IBCF).
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Although PCA is not a clustering method, it can help to reveal clusters and it’s quite good for reducing dimensionality as a feature extractor and to also visualize clusters. You can run a classifier directly on your data and record the performance. But in case you are not satisfied; try PCA by selecting the number of components at the tip of sorted eigenvalue plot. Then, run the K-means. If it produces good clusters, then PCA and classifier could do the magic.
The amount of clusters is determined by 'elbow' approach according to the value within groups sum of squares. Basically, you repeat K-means algorithm for different amount of clusters and calculate this sum of squares. If the number of clusters equal to the number of data points, then sum of squares equal 0.
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At training phase, I applied k-means clustering algorithm on a dataset (k=10). I want to apply decision tree on each cluster and generate a separate performance model for each cluster
At testing phase, I want to compare each test instance with the centroid of each cluster and  use appropriate model to classify the instance.
Is there a way I can achieve this in WEKA or WEKA API. Any link/resource will be appreciated
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Hi Johnson,
Several ways to do that; for example, you can use WEKA's KnowledgeFlow as follows:
Training set will be passed into SimpleKMeans, after that prediction model gets built using J48 based on the generated clusters. All this process is evaluated using cross-validation.
NB: The attached image shows the flow process where FilteredClassifier is containing both cluster and classification algorithms.
HTH.
Samer
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I get the number of components for my dataset using BIC but i want to know if the Silhouette coefficient method is the right option to validate my results.
Thanks!
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Silhouette analysis is based on the distance of the data point, it is very friendly to linear based clustering such as K-Mean. As a measure strategy, you can try to use it but its performance on density natural data might not be very ideal.
Meanwhile, if you want to know whether the number of components is a correct choice, may be you can try Variational Bayesian Gaussian Mixture. This is a deviation of traditional GMM which could automatically output the best component numbers.
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Thanks to Prof. Erkki Oja it is well known that a neuron using simple Hebbian Learning (including a weight decay) learns to extract the first principal component of the input data.
However, I'd like to validate my intuitions about how this generalizes to a Hebbian network which makes use of competitive learning/lateral inhibition between neurons within a layer.
So, considering I have a competitive neural network model with multiple Hebbian neurons (arranged in a layer) I would assume that the neurons roughly learn to differentiate along the first principal component.
Could anybody please validate or reject this supposition or/and provide any literature regarding that topic? Most sources only consider single or chained (Sanger's rule) Hebbian neurons.
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Thanks a lot for that answer, Amir. I don't have access to this book, but discriminating optimally among an ensemble of inputs sounds pretty much like what I expected :)
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hi there , i am trying to perform clustering on some multi-variate continuous numerical data, just wondering if any one has tried to use R in this using deep learning algorithms ? i only found that autoencoders are the unsupervised learning algorithm in unsupervised learning.
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Auto encoder left input space to feature space also simultaneously cluster  data. If you can use error for constructing data and minimum variance in clustering together you find milestone.
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I'm developing detector that searches road sign with Matlab, and the camera on the vehicle is moving. HoG-Cascade is firstly used to detect road sign candidates, as you can see on the attached image, but it has lots of "mis-detections", cuz it simply looks like rectangle as I concerned. So I trained HoG-SVM classifier to detect the arrow signs,"<" and ">", which in the road sign. The classifer detects the arrow signs based on sliding window with fixed size. The problem I'm facing is that the camera is moving so the arrow signs get larger as the road sign gets closer to camera(vehicle). Now I'd want to do "multi-scale search" with the SVM classifier, but I couldn't find any functions for that... Any help should be welcome!