Learning robust graph for clustering

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Learning robust graph for clustering Zheng Liu, Zheng Liu College of Control Science and Engineering, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this authorWei Jin, Corresponding Author Wei Jin jinweimy@zju.edu.cn College of Control Science and Engineering, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China College of Control Science and Engineering, Huzhou Institute of Zhejiang University, Huzhou, China Correspondence Wei Jin, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, 310027 Hangzhou, China. Email: jinweimy@zju.edu.cnSearch for more papers by this authorYing Mu, Ying Mu orcid.org/0000-0002-4322-2522 College of Control Science and Engineering, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this author Zheng Liu, Zheng Liu College of Control Science and Engineering, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this authorWei Jin, Corresponding Author Wei Jin jinweimy@zju.edu.cn College of Control Science and Engineering, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China College of Control Science and Engineering, Huzhou Institute of Zhejiang University, Huzhou, China Correspondence Wei Jin, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, 310027 Hangzhou, China. Email: jinweimy@zju.edu.cnSearch for more papers by this authorYing Mu, Ying Mu orcid.org/0000-0002-4322-2522 College of Control Science and Engineering, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this author First published: 16 May 2022 https://doi.org/10.1002/int.22901Read 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 Graph is a popular technique to explore the structure of data. Many related algorithms directly construct graphs based on the original data. Actually, the samples collected in real life usually contain noise. Besides, some unimportant features probably exist in high-dimensional data. Therefore, this way cannot assure a high-quality graph and furthermore brings some adverse influence to the following tasks. In this paper, we incorporate robust graph learning and dimensionality reduction into a unified framework which also seamlessly integrates the clustering task. On the basis of the framework, Euclidean distance-based robust graph (EDBRG) and self-expressiveness-based robust graph (SEBRG) are presented. Both EDBRG and SEBRG contain clustering information from which the clustering results can be obtained directly. By projecting the original data into a discriminative subspace where the negative effect of redundant features and noise is removed, EDBRG and SEBRG are informative, robust, and sparse. During the whole mapping process, the main energy of data is preserved. Finally, some data sets are adopted to test the performances of EDBRG and SEBRG. Extensive experiments illustrate that the proposed methods have many advantages for the task of clustering, comparing with the state-of-the-art algorithms. Open Research DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation