Partial multiview clustering with locality graph regularization

International Journal of Intelligent SystemsVolume 36, Issue 6 p. 2991-3010 RESEARCH ARTICLE Partial multiview clustering with locality graph regularization Huiqiang Lian, Huiqiang Lian University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorHuiying Xu, Corresponding Author Huiying Xu [email protected]zjnu.edu.cn orcid.org/0000-0002-6704-0301 College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China Correspondence Huiying Xu, College of Mathematics and Computer Science, Zhejiang Normal University, 321004 Jinhua, China. Email: [email protected]Search for more papers by this authorSiwei Wang, Siwei Wang orcid.org/0000-0001-9517-262X College of Computer, National University of Defense Technology, Changsha, ChinaSearch for more papers by this authorMiaomiao Li, Miaomiao Li orcid.org/0000-0001-7678-687X College of Electronic Information and Electrical Engineering, Changsha University, Changsha, ChinaSearch for more papers by this authorXinzhong Zhu, Xinzhong Zhu orcid.org/0000-0002-0033-5260 College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China Research Institute of Ningbo Cixing Co. Ltd, Cixi City, ChinaSearch for more papers by this authorXinwang Liu, Xinwang Liu orcid.org/0000-0001-9066-1475 College of Computer, National University of Defense Technology, Changsha, ChinaSearch for more papers by this author Huiqiang Lian, Huiqiang Lian University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorHuiying Xu, Corresponding Author Huiying Xu [email protected] orcid.org/0000-0002-6704-0301 College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China Correspondence Huiying Xu, College of Mathematics and Computer Science, Zhejiang Normal University, 321004 Jinhua, China. Email: [email protected]Search for more papers by this authorSiwei Wang, Siwei Wang orcid.org/0000-0001-9517-262X College of Computer, National University of Defense Technology, Changsha, ChinaSearch for more papers by this authorMiaomiao Li, Miaomiao Li orcid.org/0000-0001-7678-687X College of Electronic Information and Electrical Engineering, Changsha University, Changsha, ChinaSearch for more papers by this authorXinzhong Zhu, Xinzhong Zhu orcid.org/0000-0002-0033-5260 College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China Research Institute of Ningbo Cixing Co. Ltd, Cixi City, ChinaSearch for more papers by this authorXinwang Liu, Xinwang Liu orcid.org/0000-0001-9066-1475 College of Computer, National University of Defense Technology, Changsha, ChinaSearch for more papers by this author First published: 15 March 2021 https://doi.org/10.1002/int.22409Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Multiview clustering (MVC) collects complementary and abundant information, which draws much attention in machine learning and data mining community. Existing MVC methods usually hold the assumption that all the views are complete. However, multiple source data are often incomplete in real-world applications, and so on sensor failure or unfinished collection process, which gives rise to incomplete multiview clustering (IMVC). Although enormous efforts have been devoted in IMVC, there still are some urgent issues that need to be solved: (i) The locality among multiple views has not been utilized in the existing mechanism; (ii) Existing methods inappropriately force all the views to share consensus representation while ignoring specific structures. In this paper, we propose a novel method termed partial MVC with locality graph regularization to address these issues. First, followed the traditional IMVC approaches, we construct weighted semi-nonnegative matrix factorization models to handle incomplete multiview data. Then, upon the consensus representation matrix, the locality graph is constructed for regularizing the shared feature matrix. Moreover, we add the coefficient regression term to constraint the various base matrices among views. We incorporate the three aforementioned processes into a unified framework, whereas they can negotiate with each other serving for learning tasks. An effective iterative algorithm is proposed to solve the resultant optimization problem with theoretically guaranteed convergence. The comprehensive experiment results on several benchmarks demonstrate the effectiveness of the proposed method. Citing Literature Volume36, Issue6June 2021Pages 2991-3010 RelatedInformation