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Multi-view learning via multiple graph regularized generative model.pdf

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Multi-view learning via multiple graph regularized generative model.pdf

Knowledge-Based Systems 121 (2017) 153–162 Contents lists available at ScienceDirect Knowledge-Based Systems journal homepage: /locate/knosys Multi-view learning via multiple graph regularized generative model Shaokai Wang a, Eric Ke Wang a, Xutao Li a, Yunming Ye a,?, Raymond Y.K. Lau b, Xiaolin Du c a Department of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China b Department of Information Systems, City University of Hong Kong, Hong Kong c College of Computer Science, Beijing University of Technology, Beijing, China article info Article history: Received 21 June 2016 Revised 9 December 2016 Accepted 17 January 2017 Available online 3 February 2017 Keywords: Multi-view learning Generative model Manifold learning abstract Topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), have shown impressive success in many ?elds. Recently, multi-view learning via probabilistic latent semantic analysis (MVPLSA), is also designed for multi-view topic modeling. These approaches are instances of generative model, whereas they all ignore the manifold structure of data distribution, which is generally useful for preserving the nonlinear information. In this paper, we propose a novel multiple graph regularized generative model to exploit the manifold structure in multiple views. Speci?cally, we construct a nearest neighbor graph for each view to encode its corresponding manifold information. A multiple graph ensemble regularization framework is proposed to learn the optimal intrinsic manifold. Then, the manifold regularization term is incorporated into a multi-view topic model, resulting in a uni?ed objective function. The solutions are derived based on the Expectation Maximization optimization framework. Experimental results on real-world multi-view data sets demonstrate the effectiveness of our approach. ? 2017 Elsevier B.V. All rights reserved. 1. Introduction Many data sets in real wo