Multi-view learning via multiple graph regularized generative model

Shaokai Wang

doi:10.1016/j.knosys.2017.01.022

Multi-view learning via multiple graph regularized generative model

2017-04-01

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DOI：10.1016/j.knosys.2017.01.022

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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. (C) 2017 Elsevier B.V. All rights reserved.

Keywords:

Multi-view learning

Generative model

Manifold learning

Multi-view learning via multiple graph regularized generative model

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