文献类型：会议论文

英文题名：Multi-view non-negative matrix factorization by patch alignment framework with view consistency

作者：Ou, Weihua Yu, Shujian Li, Gai Lu, Jian Zhang, Kesheng Xie, Gang

第一作者：Ou, Weihua

通信作者：Ou, WH[1]

机构：[1]Guizhou Normal Univ, Sch Big Data & Comp Sci, Guiyang 550001, Peoples R China;[2]Univ Florida, Dept Elect & Comp Engn, Gainesville, FL 32601 USA;[3]Shunde Polytech, Dept Elect & Informat Engn, Foshan 528300, Peoples R China;[4]Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China;[5]Guizhou Inst Technol, Sch Informat Engn, Guiyang 550003, Peoples R China

第一机构：Guizhou Normal Univ, Sch Big Data & Comp Sci, Guiyang 550001, Peoples R China

通信机构：corresponding author), Guizhou Normal Univ, Sch Big Data & Comp Sci, Guiyang 550001, Peoples R China.

会议论文集：IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

会议日期：OCT 18-19, 2014

会议地点：Huazhong Univ Sci & Technol, Wuhan, PEOPLES R CHINA

主办单位：Huazhong Univ Sci & Technol

语种：英文

外文关键词：Multi-view non-negative matrix factorization; Patch alignment framework; Geometric structure; Locally linear embedding; View consistency

年份：2016

摘要：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 specifically, 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 views, we make the representations of views to approximate the latent representation shared by different views via considering the view consistency. We adopt the correntropy-induced metric to measure the reconstruction error and employ the half-quadratic technique to solve the optimization problem. The experimental results demonstrate the proposed method can achieve satisfactory performance compared with single-view methods and other existing multi-view NMF methods. (C) 2016 Elsevier B.V. All rights reserved.