Multi-view learning via multiple graph regularized generative model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

11 Scopus Citations
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Author(s)

  • Shaokai Wang
  • Eric Ke Wang
  • Xutao Li
  • Yunming Ye
  • Xiaolin Du

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)153-162
Journal / PublicationKnowledge-Based Systems
Volume121
Online published3 Feb 2017
Publication statusPublished - 1 Apr 2017

Abstract

Topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), have shown impressive success in many fields. 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. Specifically, 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 unified 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.

Research Area(s)

  • Generative model, Manifold learning, Multi-view learning

Citation Format(s)

Multi-view learning via multiple graph regularized generative model. / Wang, Shaokai; Wang, Eric Ke; Li, Xutao; Ye, Yunming; Lau, Raymond Y.K.; Du, Xiaolin.

In: Knowledge-Based Systems, Vol. 121, 01.04.2017, p. 153-162.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review