文献类型：期刊文献

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, Weihua

机构：[1] School of Big Data and Computer Science, Guizhou Normal University, Guiyang, 550001, China; [2] Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32601, United States; [3] Department of Electronics and Information Engineering, Shunde Polytechnic, Foshan, 528300, China; [4] School of Information Science and Engineering, Southeast University, Nanjing, 210096, China; [5] School of Information Engineering, Guizhou Institute of Technology, Guiyang, 550003, China

第一机构：School of Big Data and Computer Science, Guizhou Normal University, Guiyang, 550001, China

年份：2016

卷号：204

起止页码：116-124

外文期刊名：Neurocomputing

收录：EI(收录号：20161702281170);Scopus(收录号：2-s2.0-84963720550)

基金：This work is supported in part by the National Natural Science Foundation of China (Grant Nos. 61402122 , 61401086 and 61461008 ), and the 2014 Ph.D. Recruitment Program of Guizhou Normal University, the Outstanding Innovation Talents of Science and Technology Award Scheme of Education Department in Guizhou Province (Qianjiao KY word [2015]487), the Recruitment Program of Guizhou Institute of Technology (Grant No. XJGC20140601 ), the Natural Science Foundation of Guizhou Province of China (Grant No. [2015]2065 ).

语种：英文

外文关键词：Geometry - Alignment - Matrix algebra

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. ? 2016 Elsevier B.V.