Multiple kernel clustering with late fusion consensus local graph preserving

International Journal of Intelligent SystemsVolume 36, Issue 12 p. 7500-7521 RESEARCH ARTICLE Multiple kernel clustering with late fusion consensus local graph preserving Yujing Zhang, Yujing Zhang orcid.org/0000-0003-3209-6257 College of Computer, National University of Defense Technology, Changsha, ChinaSearch 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 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 authorEn Zhu, Corresponding Author En Zhu enzhu@nudt.edu.cn orcid.org/0000-0003-2305-7555 College of Computer, National University of Defense Technology, Changsha, China Correspondence En Zhu, College of Computer, National University of Defense Technology, Changsha 410073, China. Email: enzhu@nudt.edu.cnSearch for more papers by this author Yujing Zhang, Yujing Zhang orcid.org/0000-0003-3209-6257 College of Computer, National University of Defense Technology, Changsha, ChinaSearch 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 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 authorEn Zhu, Corresponding Author En Zhu enzhu@nudt.edu.cn orcid.org/0000-0003-2305-7555 College of Computer, National University of Defense Technology, Changsha, China Correspondence En Zhu, College of Computer, National University of Defense Technology, Changsha 410073, China. Email: enzhu@nudt.edu.cnSearch for more papers by this author First published: 31 August 2021 https://doi.org/10.1002/int.22596Read 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 Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Multiple kernel clustering (MKC) methods aim at integrating an optimal partition from a set of precalculated kernel matrices. Though achieving success in various applications, we observe that existing MKC methods: (i) lack of representation flexibility; and (ii) do not considerably preserve the locality structure in partition space. These issues may adversely affect the learning procedure of MKC, leading to unsatisfying clustering performance. In this paper, we propose a late fusion MKC method with local graph refinement to address the aforementioned issues. Different from existing MKC mechanisms, our method unifies the traditional weighted multiple kernel k-means, kernel partition, and graph construction into a single optimization procedure. The local graph is utilized to preserve the locality information in partition space and therefore all of the counterparts can be boosted for mutual clustering improvements. By this way, our approach enhances the local graph structure in partition space and enjoys more flexible kernel representations, leading to significant clustering improvements. Moreover, a three-step alternate algorithm is developed to solve the resultant optimization problem with proved convergence. Extensive experiments are conducted on several multiple kernel benchmark datasets to compare the proposed algorithm with the state-of-the-art ones, and the results well demonstrate its effectiveness and superiority. Volume36, Issue12December 2021Pages 7500-7521 RelatedInformation