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Multi-view non-negative matrix factorization by patch alignment framework with view consistency.pdf

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Multi-view non-negative matrix factorization by patch alignment framework with view consistency.pdf

Neurocomputing 204 (2016) 116–124 Contents lists available at ScienceDirect Neurocomputing journal homepage: /locate/neucom Multi-view non-negative matrix factorization by patch alignment framework with view consistency Weihua Ou a,n, Shujian Yu b, Gai Li c, Jian Lu d, Kesheng Zhang e, Gang Xie a a School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550001, China b Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32601, USA c Department of Electronics and Information Engineering, Shunde Polytechnic, Foshan 528300, China d School of Information Science and Engineering, Southeast University, Nanjing 210096, China e School of Information Engineering, Guizhou Institute of Technology, Guiyang 550003, China article info Article history: Received 9 March 2015 Received in revised form 13 September 2015 Accepted 15 September 2015 Available online 9 April 2016 Keywords: Multi-view non-negative matrix factorization Patch alignment framework Geometric structure Locally linear embedding View consistency abstract Multi-view non-negative matrix factorization (NMF) has been developed to learn the latent representation from multi-view non-negative data in recent years. To make the representation more meaningful, previous works mainly exploit either the consensus information or the complementary information from different views. However, the latent local geometric structure of each view is always ignored. In this paper, we develop a novel multi-view NMF by patch alignment framework with view consistency. Different from previous works, we take the local geometric structure of each view into consideration, and penalize the disagreement of different views at the same time. More speci?cally, given a data in each view, we construct a local patch utilizing locally linear embedding to preserve its local geometrical structure, and obtain the global representation under the whole alignment strategy. Meanwhile, for different view