Ming Yang’s research while affiliated with Zhejiang University and other places

Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper introduces two categories for multi-view representation learning: multi-view representation alignment and multi-view representation fusion. Consequently, we first review the representative methods and theories of multi-view representation learning based on the perspective of alignment, such as correlation-based alignment. Representative examples are canonical correlation analysis (CCA) and its several extensions. Then from the perspective of representation fusion we investigate the advancement of multi-view representation learning that ranges from generative methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to neural network-based methods including multi-modal autoencoders, multi-view convolutional neural networks and multi-modal recurrent neural networks. Further, we also investigate several important applications of multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical foundation and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.

For many supervised learning problems, limited training samples and incomplete labels are two difficult challenges, which usually lead to degenerated performance on label prediction. To improve the generalization performance, in this paper, we propose Doubly Regularized Multi-Label learning (DRML) by exploiting feature network and label network regularization simultaneously. In more details, the proposed algorithm first constructs a feature network and a label network with marginalized linear denoising autoencoder in data feature set and label set, respectively, and then learns a robust predictor with the feature network and the label network regularization simultaneously. While DRML is a general method for multi-label learning, in the evaluations we focus on the specific application of multi-label text tagging. Extensive evaluations on three benchmark data sets demonstrate that DRML outstands with a superior performance in comparison with some existing multi-label learning methods.

Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper first reviews the root methods and theories on multi-view representation learning, especially on canonical correlation analysis (CCA) and its several extensions. And then we investigate the advancement of multi-view representation learning that ranges from shallow methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to deep methods including multi-modal restricted Boltzmann machines, multi-modal autoencoders, and multi-modal recurrent neural networks. Further, we also provide an important perspective from manifold alignment for multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical basis and current developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.

Tagging has become increasingly important in many real-world applications noticeably including web applications, such as web blogs and resource sharing systems. Despite this importance, tagging methods often face difficult challenges such as limited training samples and incomplete labels, which usually lead to degenerated performance on tag prediction. To improve the generalization performance, in this paper, we propose Regularized Marginalized Cross-View learning (RMCV) by jointly modeling on attribute noise and label noise. In more details, the proposed model constructs infinite training examples with attribute noises from known exponential-family distributions and exploits label noise via marginalized denoising autoencoder. Therefore, the model benefits from its robustness and alleviates the problem of tag sparsity. While RMCV is a general method for learning tagging, in the evaluations we focus on the specific application of multi-label text tagging. Extensive evaluations on three benchmark data sets demonstrate that RMCV outstands with a superior performance in comparison with state-of-the-art methods.

In this paper we study the problem of recommending scientific articles to users in an online community with a new perspective of considering topic regression modeling and articles relational structure analysis simultaneously. First, we present a novel topic regression model, the topic regression matrix factorization (tr-MF), to solve the problem. The main idea of tr-MF lies in extending the matrix factorization with a probabilistic topic modeling. In particular, tr-MF introduces a regression model to regularize user factors through probabilistic topic modeling under the basic hypothesis that users share similar preferences if they rate similar sets of items. Consequently, tr-MF provides interpretable latent factors for users and items, and makes accurate predictions for community users. To incorporate the relational structure into the framework of tr-MF, we introduce relational matrix factorization. Through combining tr-MF with the relational matrix factorization, we propose the topic regression collective matrix factorization (tr-CMF) model. In addition, we also present the collaborative topic regression model with relational matrix factorization (CTR-RMF) model, which combines the existing collaborative topic regression (CTR) model and relational matrix factorization (RMF). From this point of view, CTR-RMF can be considered as an appropriate baseline for tr-CMF. Further, we demonstrate the efficacy of the proposed models on a large subset of the data from CiteULike, a bibliography sharing service dataset. The proposed models outperform the state-of-the-art matrix factorization models with a significant margin. Specifically, the proposed models are effective in making predictions for users with only few ratings or even no ratings, and support tasks that are specific to a certain field, neither of which has been addressed in the existing literature.

In this paper, we study the multi-task learning problem with a new perspective of considering the link structure of data and task relationship modeling simultaneously. In particular, we first introduce the Matrix Generalized Inverse Gaussian (MGIG) distribution and define a Matrix Gaussian Matrix Generalized Inverse Gaussian (MG-MGIG) prior. Based on this prior, we propose a novel multi-task learning algorithm, the Bayesian Multi-task Relationship Learning (BMTRL) algorithm. To incorporate the link structure into the framework of BMTRL, we propose link constraints between samples. Through combining the BMTRL algorithm with the link constraints, we propose the Bayesian Multi-task Relationship Learning with Link Constraints (BMTRL-LC) algorithm. To make the computation tractable, we simultaneously use a convex optimization method and sampling techniques. In particular, we adopt two stochastic EM algorithms for BMTRL and BMTRL-LC, respectively. The experimental results on Cora dataset demonstrate the promise of the proposed algorithms.

Non-negative Matrix Factorization (NMF) has become increasingly popular in many applications that require data mining techniques such as information retrieval, computer vision, and pattern recognition. NMF aims at approximating the original data matrix in a high dimensional space with the product of two non-negative matrices in a lower dimensional space. In many applications with high dimensional data such as text, data often have a global geometric structure, which typically may not be directly derived from the local information. But the existing literature of NMF completely ignores this problem. This paper proposes a novel matrix factorization algorithm called Coordinate Ranking regularized NMF (CR-NMF) in order to address this problem. The idea of the proposed algorithm is to combine NMF and manifold ranking to encode both local and global geometric structures of the data. Experimental results on two real-world datasets demonstrate the superiority of this algorithm.

We study the problem of recommending scientific articles to users in an online community and present a novel matrix factorization model, the topic regression Matrix Factorization (tr-MF), to solve the problem. The main idea of tr-MF lies in extending the matrix factorization with a probabilistic topic modeling. Instead of regularizing item factors through the probabilistic topic modeling as in the framework of the CTR model, tr-MF introduces a regression model to regularize user factors through the probabilistic topic modeling under the basic hypothesis that users share the similar preferences if they rate similar sets of items. Consequently, tr-MF provides interpretable latent factors for users and items, and makes accurate predictions for community users. Specifically, it is effective in making predictions for users with only few ratings or even no ratings, and supports tasks that are specific to a certain field, neither of which is addressed in the existing literature. Further, we demonstrate the efficacy of tr-MF on a large subset of the data from CiteULike, a bibliography sharing service dataset. The proposed model outperforms the state-of-the-art matrix factorization models with a significant margin.

With the rapid development of social networks, tagging has become an important means responsible for such rapid development. A robust tagging method must have the capability to meet the two challenging requirements: limited labeled training samples and noisy labeled training samples. In this paper, we investigate this challenging problem of learning with limited and noisy tagging and propose a discriminative model, called SpSVM-MC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label constraints into the optimization. While SpSVM-MC is a general method for learning with limited and noisy tagging, in the evaluations we focus on the specific application of noisy image tagging with limited labeled training samples on a benchmark dataset. Theoretical analysis and extensive evaluations in comparison with state-of-the-art literature demonstrate that SpSVM-MC outstands with a superior performance.

In many real-world applications, tagging is imperfect: incomplete, inconsistent, and error-prone. Solutions to this problem will generate societal and technical impacts. In this paper, we investigate this arguably new problem: learning from imperfect tagging. We propose a general and effective learning scheme called the Multi-view Imperfect Tagging Learning (MITL) to this problem. The main idea of MITL lies in extracting the information of the imperfectly tagged training dataset from multiple views to differentiate the data points in the role of classification. Further, a novel discriminative classification method is proposed under the framework of MITL, which explicitly makes use of the given multiple labels simultaneously as an additional feature to deliver a more effective classification performance than the existing literature where one label is considered at a time as the classification target while the rest of the given labels are completely ignored at the same time. The proposed methods can not only complete the incomplete tagging but also denoise the noisy tagging through an inductive learning. We apply the general solution to the problem with a more specific context - imperfect image annotation, and evaluate the proposed methods on a standard dataset from the related literature. Experiments show that they are superior to the peer methods on solving the problem of learning from imperfect tagging in cross-media.