文献类型：会议论文

英文题名：Multi-view Embedding Learning via Robust Joint Nonnegative Matrix Factorization

作者：Ou, Weihua Zhang, Kesheng You, Xinge Long, Fei

第一作者：Ou, Weihua

通信作者：Ou, WH[1]

机构：[1]Guizhou Normal Univ, Sch Math & Comp Sci, Guiyang 550001, Peoples R China;[2]Guizhou Inst Technol, Sch Infomat Engn, Guiyang 550003, Peoples R China;[3]Huazhong Univ Sci & Technol, Dept Elect & Informat Engn, Wuhan 430074, Peoples R China;[4]Guizhou Inst Technol, Coll Informat Engn, Guiyang 550003, Peoples R China;[5]Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China

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

通信机构：corresponding author), Guizhou Normal Univ, Sch Math & 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：Classification (of information) - Clustering algorithms - Embeddings - Factorization

年份：2014

摘要：Real data often are comprised of multiple modalities or different views, which provide complementary and consensus information to each other. Exploring those information is important for the multi-view data clustering and classification. Multiview embedding is an effective method for multiple view data which uncovers the common latent structure shared by different views. Previous studies assumed that each view is clean, or at least there are not contaminated by noises. However, in real tasks, it is often that every view might be suffered from noises or even some views are partially missing, which renders the traditional multi-view embedding algorithm fail to those cases. In this paper, we propose a novel multi-view embedding algorithm via robust joint nonnegative matrix factorization. We utilize the correntropy induced metric to measure the reconstruction error for each view, which are robust to the noises by assigning different weight for different entries. In order to uncover the common subspace shared by different views, we define a consensus matrix subspace to constrain the disagreement of different views. For the non-convex objective function, we formulate it into half quadratic minimization and solve it via update scheme efficiently. The experiments results show its effectiveness and robustness in multiview clustering.