Guang-Bin Huang

• PhD
• Professor (Full) at Nanyang Technological University

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to sur...

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to sur...

Objective: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions. To rapidly evaluate the tDCS-induced current density in near real-time, this paper proposes a deep learning-based emulator, named DeeptDCS. Methods: The emulator lev...

Accurate motion object detection (MOD) using in-vehicle cameras in driving vehicles is a challenging task. Several deep learning based motion segmentation approaches have been reported based on the interpretable optical flow feature. However, the interpretable optical flow feature has not been explored by object-level MOD approaches. In this paper,...

Currently, illegal parking detection tasks are mainly achieved through manually checking by enforcement officers on patrol or using Closed-Circuit Television (CCTV) cameras. However, these methods either need high human labour costs or demand installation costs and procedures. Therefore, illegal parking detection solutions, which can reduce signifi...

Objective: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions. To rapidly evaluate the tDCS-induced current density in near real-time, this paper proposes a deep learning-based emulator, named DeeptDCS. Methods: The emulator levera...

The Tracking-by-segmentation framework is widely used in visual tracking to handle severe appearance change such as deformation and occlusion. Tracking-by-segmentation methods first segment the target object from the background, then use the segmentation result to estimate the target state. In existing methods, target segmentation is formulated as...

In this paper, we propose a novel transductive pseudo-labeling based method for deep semi-supervised image recognition. Inspired from the superiority of pseudo labels inferred by label propagation compared with those inferred from network, we argue that information flow from labeled data to unlabeled data should be kept noiseless and with minimum l...

Adaptively adjusting the graph, by taking the clustering capability into consideration, has become popular in graph-based clustering methods and extended to multiview clustering problem. Existing methods learn the graph from pairwise distances of the data in the original space or a linearly projected space, which requires that those representations...

Semi-supervised learning has largely alleviated the strong demand for large amount of annotations in deep learning. However, most of the methods have adopted a common assumption that there is always labeled data from the same class of unlabeled data, which is impractical and restricted for real-world applications. In this research work, our focus i...

Considering that vehicle exhaust contributes the majority of nitrogen oxides (NOx), which is harmful to environment and climate it is important to measure NOx concentrations in sustainable developments. This paper proposes to apply spectroscopic gas sensing methods and an innovative deep learning network algorithm for obtaining high-precision NOx d...

Extreme learning machine (ELM) is a popular method in machine learning with extremely few parameters, fast learning speed and model efficiency. Unsupervised feature learning based ELM receives rising research focus. Recently the ELM auto-encoder (ELM-AE) was proposed for this task, which develops the ELM based compact feature learning without sacri...

Extreme Learning Machine (ELM) is a powerful and favorable classifier used in various applications due to its fast speed and good generalization capability. However, when dealing with complex visual tasks, the shallow architecture of ELM makes it infeasible to have good performance when raw image data are directly fed in as input. Therefore, severa...

Principal component analysis network (PCANet), as an unsupervised shallow network, demonstrates noticeable effectiveness on datasets of various volumes. It carries a two-layer convolution with PCA as filter learning method, followed by a block-wise histogram post-processing stage. Following the structure of PCANet, extreme learning machine auto-enc...

Recently, learning data representations have been investigated to reduce the dependences of human intervention and improve the performance of machine fault diagnosis. However, most of the representation learning methods are computationally intensive due to complex training procedures. Extreme learning machine is well-known for its fast training spe...

In this paper an effective graph learning method is proposed for clustering based on adaptive graph regularizations. Many graph learning methods focus on optimizing a global constraint on sparsity, low-rankness or weighted pair-wise distances, but they often fail to consider local connectivities. We demonstrate the importance of locality by general...

Recently, deep learning-based representation learning methods have attracted increasing attention in machine fault diagnosis. However, few existing methods consider the geometry of data samples. In this paper, we propose a novel method to obtain representations that preserve the geometry of input data. More specifically, we formulate two cost funct...

The prosperity of artificial intelligence has aroused intensive interests in intelligent/autonomous navigation, in which path prediction is a key functionality for decision supports, e.g. route planning, collision warning, and traffic regulation. For maritime intelligence, Automatic Identification System (AIS) plays an important role because it rec...

Extreme Learning Machine (ELM) is a popular method in machine learning with extremely few parameters, fast learning speed and model efficiency. While a significant drawback is that ELM is restricted by its single-layer structure and prized analytic solution. If simply stacking more layers, analytic solution of ELM will be intractable. Then gradient...

Advanced chemometric analysis is required for rapid and reliable determination of physical and/or chemical components in complex gas mixtures. Based on infrared (IR) spectroscopic/sensing techniques, we propose an advanced regression model based on the extreme learning machine (ELM) algorithm for quantitative chemometric analysis. The proposed mode...

For data with various complicated distribution in the original feature space, it is difficult to find the clusters of the data. Extreme learning machine (ELM) is famous for its universal approximation capability and the hidden space created by random nonlinear feature mapping. Existing ELM based clustering methods address this by constructing an em...

Dictionary learning is a widely adopted approach for image classification. Existing methods focus either on finding a dictionary that produces discriminative sparse representation, or on enforcing priors that best describe the dataset distribution. In many cases, the dataset size is often small with large intra-class variability and nondiscriminati...

The articles in this special section focuses on machine learning (ML) and signal processing algorithms for bio-inspired computing. The articles bring together key researchers in this area to provide readers of IEEE Signal Processing Magazine with up-to-date and survey-style articles on algorithmic, hardware, and neuroscience perspectives on the sta...

Recently, preserving geometry information of data while learning representations have attracted increasing attention in intelligent machine fault diagnosis. Existing geometry preserving methods require to predefine the similarities between data points in the original data space. The predefined affinity matrix, which is also known as the similarity...

Convolutional dictionary learning (CDL) aims to learn a structured and shift-invariant dictionary to decompose signals into sparse representations. While yielding superior results compared to traditional sparse coding methods on various signal and image processing tasks, most CDL methods have difficulties handling large data, because they have to p...

Extreme Learning Machine (ELM) feature representation has been drawing increasing attention, and most of the previous works devoted to learning discriminative features. However, we argue that such kind of features suffer from “categories bias” in target detection tasks, where the scope of the negatives (i.e., backgrounds) is naturally broader than...

In many practical transfer learning scenarios, the feature distribution is different across the source and target domains (i.e., nonindependent identical distribution). Maximum mean discrepancy (MMD), as a domain discrepancy metric, has achieved promising performance in unsupervised domain adaptation (DA). We argue that the MMD-based DA methods ign...

In many practical transfer learning scenarios, the feature distribution is different across the source and target domains (i.e. non-i.i.d.). Maximum mean discrepancy (MMD), as a domain discrepancy metric, has achieved promising performance in unsupervised domain adaptation (DA). We argue that MMD-based DA methods ignore the data locality structure,...

The articles in this special section aim to promote novel research investigations in low power and low latency of smart chips and smart hardware for machine learning and biologically-plausible learning.

Target coding is an indispensable part of supervised learning. Currently, the assumption of the most popular target coding like one-hot (one-of-K) coding is that targets are independent. However, this assumption is limited due to the complex relationship between targets. In this paper, we will explore the effects of kinds of target coding methods o...

Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exists significant statistical differences between distortion-free na...

Extreme learning machine (ELM) has been extensively studied, due to its fast training and good generalization. Unfortunately, the existing ELM-based feature representation methods are uncompetitive with state-of-the-art deep neural networks (DNNs) when conducting some complex visual recognition tasks. This weakness is mainly caused by two critical...

Word Embeddings are low-dimensional distributed representations that encompass a set of language modeling and feature learning techniques from Natural Language Processing (NLP). Words or phrases from the vocabulary are mapped to vectors of real numbers in a low-dimensional space. In previous work, we proposed using an Extreme Learning Machine (ELM)...

In recent years, maritime safety and efficiency become very important across the world. Automatic Identification System (AIS) tracks vessel movement by onboard transceiver and terrestrial and/or satellite base stations. The data collected by AIS contain broadcast kinematic information and static information. Both of them are useful for maritime ano...

The Smile is one of the most common facial expressions, and it serves as an indicator of the positive emotion. Many feature extraction methods have been proposed for detecting a smile in an unconstrained scene. However, most of the existing feature descriptors are too large and not effective to be applied to distinguish smile and non-smile in the r...

Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditi...

With the development of deep neural networks, researchers have developed lots of algorithms related to face and achieved comparable results to human-level performance on several databases. However, few feature extraction models work well in the real world when the subject which is to be recognized has limited samples, for example, only one ID photo...

Adaptive graph learning methods for clustering, which adjust a data similarity matrix while taking into account its clustering capability, have drawn increasing attention in recent years due to their promising clustering performance. Existing adaptive graph learning methods are based on either original data or linearly projected data and thus rely...

In this paper, we develop a novel algorithm for classifying foreign object debris (FOD) based on the integrated visual features and extreme learning machine (ELM). After image preprocessing, various types of characteristics of the FOD image such as the color names, the scale-invariant feature transform (SIFT), and the histograms of oriented gradien...

With the development of deep neural networks, researchers have developed lots of algorithms related to face and achieved comparable results to human-level performance on several databases. However, few feature extraction models work well in the real world when the subject which is to be recognized has limited samples, for example, only one ID photo...

Study objectives: Automated sleep staging has been previously limited by a combination of clinical and physiological heterogeneity. Both factors are in principle addressable with large data sets that enable robust calibration. However, the impact of sample size remains uncertain. The objectives are to investigate the extent to which machine learni...

With the exponential growth of data and complexity of systems, fast machine learning/artificial intelligence and computational intelligence techniques are highly required. Many conventional computational intelligence techniques face bottlenecks in learning (e.g., intensive human intervention and convergence time) [item 1) in the Appendix]. However,...

Clustering generic data, i.e., data not specific to a particular field, is a challenging problem due to their diverse complex structures in the original feature space. Traditional approaches address this problem by complementing clustering with feature learning methods, which either capture the intrinsic structure of the data or represent the data...

Most of the existing image blurriness assessment algorithms are proposed based on measuring image edge width, gradient, high-frequency energy, or pixel intensity variation. However, these methods are content sensitive with little consideration of image content variations, which causes variant estimations for images with different contents but same...

Spatiotemporal fusion is important in providing high spatial resolution earth observations with a dense time series, and recently, learning-based fusion methods have been attracting broad interest. These algorithms project image patches onto a feature space with the enforcement of a simple mapping to predict the fine resolution patches from the cor...

A biological neural network is constituted by numerous subnetworks and modules with different functionalities. For an artificial neural network, the relationship between a network and its subnetworks is also important and useful for both theoretical and algorithmic research, i.e. it can be exploited to develop incremental network training algorithm...

Polychronous neuronal group (PNG), a type of cell assembly, is one of the putative mechanisms for neural information representation. According to the reader-centric definition, some readout neurons can become selective to the information represented by polychronous neuronal groups under ongoing activity. Here, in computational models, we show that...

Presentation on "25th Annual Computational Neuroscience Meeting: CNS-2016 " BMC Neuroscience 17, 112-113 (2016).

In recent years, maritime safety and efficiency become more and more important across the world. Automatic Identification System (AIS) tracks vessel movement by onboard transceiver and terrestrial and/or satellite base station. The data collected by AIS contains broadcast kinematic information and static information. Both of them are useful for ano...

The Automatic Identification System (AIS) tracks vessel movement by means of electronic exchange of navigation data between vessels, with onboard transceiver, terrestrial and/or satellite base stations. The gathered data contains a wealth of information useful for maritime safety, security and efficiency. This paper surveys AIS data sources and rel...

The Automatic Identification System (AIS) tracks vessel movement by means of electronic exchange of navigation data between vessels, with onboard transceiver, terrestrial and/or satellite base stations. The gathered data contains a wealth of information useful for maritime safety, security and efficiency. This paper surveys AIS data sources and rel...

Data may often contain noise or irrelevant information which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms such as Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), random projection (RP) and auto-encoder (AE) is to reduce the noise or irrel...

Extreme learning machine (ELM) solves regression and classification problems efficiently. However, the solution provided is dense and requires plenty of storage space and testing time. A sparse ELM has been proposed for classification in [1]. However, it is not applicable for regression problems. In this paper, we propose a sparse ELM for regressio...

ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment Ion channels are fundamental constituents determining the function of single neurons and neuronal circuits. To understand their complex interactions, the field of computational modeling has proven essential: since its emergence, thousands...

Generic object recognition is to classify the object to a generic category. Intra-class variabilities cause big troubles for this task. Traditional methods involve plenty of pre-processing steps, like model construction, feature extraction, etc. Moreover, these methods are only effective for some specific dataset. In this paper, we propose to use l...

Real-time driver distraction detection is the core to many distraction countermeasures and fundamental for constructing a driver-centered driver assistance system. While data-driven methods demonstrate promising detection performance, a particular challenge is how to reduce the considerable cost for collecting labeled data. This paper explored semi...

The data-oriented applications have introduced increased demands on memory capacity and bandwidth, which raises the need to rethink the architecture of the current computing platforms. The logic-in-memory architecture is highly promising as future logic-memory integration paradigm for high throughput data-driven applications. From memory technology...

In this paper, we proposed a driver drowsiness detection method for which only eyelid movement information was required. The proposed method consists of two major parts. 1) In order to obtain accurate eye openness estimation, a vision-based eye openness recognition method was proposed to obtain an regression model that directly gave degree of eye o...

Big dimensional data is a growing trend that is emerging in many real world contexts, extending from web mining, gene expression analysis, protein-protein interaction to high-frequency financial data. Nowadays, there is a growing consensus that the increasing dimensionality poses impeding effects on the performances of classifiers, which is termed...

Recently the state-of-the-art facial age estimation methods are almost originated from solving complicated mathematical optimization problems and thus consume huge quantities of time in the training process. To refrain from such algorithm complexity while maintaining a high estimation accuracy, we propose a multifeature extreme ordinal ranking mach...

Distraction was previously studied within each dimension separately, i.e., physical, cognitive and visual. However real-world activities usually involve multiple distraction dimensions in terms of brain resources that might conflict with the driving task. This brings difficulties for classifying dimension/type of distraction even for human experts....

A huge number of videos are posted every day on social media platforms such as Facebook and YouTube. This makes the Internet an unlimited source of information. In the coming decades, coping with such information and mining useful knowledge from it will be an increasingly difficult task. In this paper, we propose a novel methodology for multimodal...

The increasing number of demanding consumer digital multimedia applications has boosted interest in no-reference (NR) image quality assessment (IQA). In this paper, we propose a perceptual NR blur evaluation method using a new machine learning technique, i.e., extreme learning machine (ELM). The proposed metric, Blind Image Blur quality Evaluator (...

Learning representations from massive unlabelled data is a topic for high-level tasks in many applications. The recent great improvements on benchmark data sets, which are achieved by increasingly complex unsupervised learning methods and deep learning models with many parameters, usually requiring many tedious tricks and much expertise to tune. Ad...

Due to the precise spike timing in neural coding, spiking neural network (SNN) possesses richer spatiotemporal dynamics compared to neural networks with firing rate coding. One of the distinct features of SNN, polychronous neuronal group (PNG), receives much attention from both computational neuroscience and machine learning communities. However, a...

The emergent machine learning technique—extreme learning machines (ELMs)—has become a hot area of research over the past years, which is attributed to the growing research activities and significant contributions made by numerous researchers around the world. Recently, it has come to our attention that a number of misplaced notions and misunderstan...

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signa...

The articles in this special issue are dedicated to new trends of Learning in the field of computational intelligence. Over the past few decades, conventional computational intelligence techniques faced severe bottlenecks in terms of algorithmic learning. Particularly, in the areas of big data computation, brain science, cognition and reasoning, it...

Extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks (SLFNs), provides efficient unified learning solutions for the applications of feature learning, clustering, regression and classification. Different from the common understanding and tenet that hidden neurons of neural ne...

Ship detection on spaceborne images has attracted great interest in the applications of maritime security and traffic control. Optical images stand out from other remote sensing images in object detection due to their higher resolution and more visualized contents. However, most of the popular techniques for ship detection from optical spaceborne i...

Extreme learning machine (ELM), as a new learning framework, draws increasing attractions in the areas of large-scale computing, high-speed signal processing, artificial intelligence, and so on. ELM aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism and represents a suite of machine...