Jianwen Tao

ZJU NIT

Learning an effective visual classifier from few labeled samples is a challenging problem, which has motivated the multi-source adaptation scheme in machine learning. While the advantages of multi-source adaptation have been widely recognized, there still exit three major limitations in extant methods. Firstly, how to effectively select the discrim...

Learning a visual category with few labeled samples is a challenging problem in machine learning, which has motivated the multi-source adaptation learning technique, which exploits to transfer multiple prior discriminative models to target domain. Under this paradigm, however, different visual features at hand cannot be effectively exploited to rep...

For a domain adaptation learning problem, how to minimize the distribution mismatch between different domains is one of key challenges. In real applications, it is reasonable to obtain an optimal latent space for both domains so as to reduce the domain distribution discrepancy as much as possible. We therefore propose in this paper a Robust Latent...

While feature selection has recently received much research attention, less or limited effort has been made on improving the performance of feature selection by leveraging the shared knowledge from other related domains. Besides, multi-source adaptation embedding by exploiting the correlation information among domain features and distributions has...

How to guarantee the robustness of multi-source adaptation visual classification is an important challenge in current visual learning community. To this end, we address in this paper the problem of robust visual classification with few labeled samples from the target domain of interest by leveraging multiple prior source models. Motivated by the re...

Most of existing domain adaptation learning (DAL) methods relies on a single source domain to learn a classifier with well-generalized performance for the target domain of interest, which may lead to the so-called negative transfer problem. To this end, many multi-source adaptation methods have been proposed. While the advantages of using multi-sou...

While the advantages of using information from multi-source domains for establishing an adaptation model have been widely recognized, how to boosting the robustness of the learning model has only recently received attention. For achieving a robust adaptation learning model for visual classification tasks, by exploiting multi-source adaptation regul...

Domain adaptation image classification addresses the problem of adapting the image distribution of the source domain to the target domain for an effective learning task, where the classification objective is intended but the data distributions are different. However, corrupted data (e.g. noise and outliers, which exist universally in real-world dom...

Nonnegative matrix factorization (NMF) has been successfully used in different applications including computer vision, pattern recognition and text mining. NMF aims to decompose a data matrix into the product of two matrices (respectively denoted as the basis vectors and the encoding vectors), whose entries are constrained to be nonnegative. Unlike...

While classical classification methods such as support vector machine and its extensions (SVMs) obtain strong generalization capability by maximizing the separation margin between binary classes, they are usually sensitive to affine (or scaling) transformation of data. The optimal solutions of SVMs may be misled by the spread of data and preferenti...

In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this en...

Due to the rapid increase of available web services over the Internet, service-oriented architecture has been regarded as one of the most promising web technologies. Moreover, enterprises are able to employ outsourcing software to build and publish their business applications as services, the latter can be accessible via the Web by other people or...

Recently, domain adaptation learning (DAL) has shown surprising performance by utilizing labeled samples from the source (or auxiliary) domain to learn a robust classifier for the target domain of the interest which has a few or even no labeled samples. In this paper, by incorporating classical graph-based transductive SSL diagram, a novel DAL meth...

Domain adaptation learning (DAL) is a novel and effective technique to address pattern classification problems where the prior information for training is unavailable or insufficient. Its effectiveness depends on the discrepancy between the two distributions that respectively generate the training data for the source domain and the testing data for...

The classic support vector machine(SVM) can not efficiently exploit the local information of data points, which is useful for pattern recognition. Therefore, a so-called local learning based support vector machine is presented to address those problems mentioned above, which makes full use of the local information such as intra-locality scatter and...

Domain adaptation learning (DAL) methods have shown promising results by utilizing labeled samples from the source (or auxiliary) domain(s) to learn a robust classifier for the target domain which has a few or even no labeled samples. However, there exist several key issues which need to be addressed in the state-of-theart DAL methods such as suffi...

Domain adaptation learning is a novel effective technique to address pattern classification, in which the prior information for training a learning model is unavailable or insufficient. To minimize the distribution discrepancy between the source domain and target domain is one of the key factors. However, domain adaptation learning may not work wel...

In the process of oil displacement of ASP (Alkali/Surfactant/Polymer) flooding, when Alkali interacts with the fluid and minerals of the reservoir, the alkali is subject to be consumed. The consumption regularity is the key factor affecting ASP ingredient, injection plan, scaling regularity for production wells and oil displacement effectiveness. T...

Mining user preferences plays a critical role in many important applications such as customer relationship management, product and personalized service recommendation. Although of great potential, to the best of our knowledge, the problem of mining user preferences from positive and negative examples has not been explored before. In this paper, we...