Graph Regularized Multi-view Nonnegative Matrix Factorization with Structured Sparsity

Peng Jinye; Luo Peng

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Graph Regularized Multi-view Nonnegative Matrix Factorization with Structured Sparsity

Peng Jinye *,Luo Peng

College of Information and Technology, Northwest University of China, Xi'an, CN 710127

*Correspondence author

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Funding: Doctoral Program of Higher Education of China （No.Grant No. 20116102110027）

Opened online:26 November 2015

Accepted by: none

Citation: Peng Jinye,Luo Peng.Graph Regularized Multi-view Nonnegative Matrix Factorization with Structured Sparsity[OL]. [26 November 2015] http://en.paper.edu.cn/en_releasepaper/content/4663805

Nonnegative matrix factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of data whose representation may be parts-based in human brain. On the other hand, many real-world datasets are represented by multiple features or modalities which often provide compatible and complementary information to each other. To integrate information from multiple features in the unsupervised setting, we propose a novel Graph regularized multi-view NMF with structured sparsity (GSSNMF) for data representation. The key idea is to learn a common latent space across different views which (1) captures the semantic relationships between data items through graph regularization, and (2) allow each latent factor to be associated with a subset of views via sparseness constraints. In this way, GSSNMF could capture flexible conceptual patterns hidden in multi-view features. Experiments on two real-world datasets demonstrate the effectiveness of the proposed algorithm.

Keywords:Multi-view learning; Nonnegative matrix factorization;Structured sparsity

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