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Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>\n            <jats:bold>Graph-based Multi-View Clustering (GMVC)<\/jats:bold>\n            has received extensive attention due to its ability to capture the neighborhood relationship among data points from diverse views. However, most existing approaches construct similarity graphs from the original multi-view data, the accuracy of which heavily and implicitly relies on the quality of the original multiple features. Moreover, previous methods either focus on mining the multi-view commonality or emphasize on exploring the multi-view individuality, making the rich information contained in multiple features cannot be effectively exploited. In this work, we design a novel GMVC framework via c\n            <jats:bold>O<\/jats:bold>\n            mmo\n            <jats:bold>N<\/jats:bold>\n            ality and\n            <jats:bold>I<\/jats:bold>\n            ndividuality disc\n            <jats:bold>O<\/jats:bold>\n            vering in late\n            <jats:bold>N<\/jats:bold>\n            t subspace (\n            <jats:bold>ONION<\/jats:bold>\n            ), seeking for a robust and discriminative subspace representation compatible across multiple features for GMVC. To be specific, our method simultaneously formulates the unsupervised sparse feature selection and the robust subspace extraction, as well as the target graph learning in a unified optimization model, which can help the learning of the discriminative subspace representation and the target graph in a mutual reinforcement manner. Meanwhile, we manipulate the target graph by an explicit structural penalty, rendering the connected components in the graph directly reveal clusters. Experimental results on seven benchmark datasets demonstrate the effectiveness of our proposed method.\n          <\/jats:p>","DOI":"10.1145\/3568684","type":"journal-article","created":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T13:23:28Z","timestamp":1666704208000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["ONION: Joint Unsupervised Feature Selection and Robust Subspace Extraction for Graph-based Multi-View Clustering"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1085-9084","authenticated-orcid":false,"given":"Zhibin","family":"Gu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University and School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5922-9358","authenticated-orcid":false,"given":"Songhe","family":"Feng","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University and School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8638-6818","authenticated-orcid":false,"given":"Ruiting","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University and School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6280-3413","authenticated-orcid":false,"given":"Gengyu","family":"Lyu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology and Engineering Research Center of Intelligence Perception and Autonomous Control, Ministry of Education, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298657"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5756"},{"key":"e_1_3_2_4_2","first-page":"1","article-title":"Low-rank tensor based proximity learning for multi-view clustering","author":"Chen Man-Sheng","year":"2022","unstructured":"Man-Sheng Chen, Chang-Dong Wang, and Jian-Huang Lai. 2022. 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