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Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>\n            Multi-view clustering, which aims at boosting the clustering performance by leveraging the individual information and the common information of multi-view data, has gained extensive consideration in recent years. However, most existing multi-view clustering algorithms either focus on extracting the multi-view individuality or emphasize on exploring the multi-view commonality, neither of which can fully utilize the comprehensive information from multiple views. To this end, we propose a novel algorithm named\n            <jats:bold>V<\/jats:bold>\n            iew-specific and\n            <jats:bold>C<\/jats:bold>\n            onsensus\n            <jats:bold>G<\/jats:bold>\n            raph\n            <jats:bold>A<\/jats:bold>\n            lignment (VCGA) for multi-view clustering, which simultaneously formulates the multi-view individuality and the multi-view commonality into a unified framework to effectively partition data points. To be specific, the VCGA model constructs the view-specific graphs and the shared graph from original multi-view data and hidden latent representation, respectively. Furthermore, the view-specific graphs of different views and the consensus graph are aligned into an informative target graph, which is employed as a crucial input to the standard spectral clustering method for clustering. Extensive experimental results on six benchmark datasets demonstrate the superiority of our method against other state-of-the-art clustering algorithms.\n          <\/jats:p>","DOI":"10.1145\/3532612","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T11:56:58Z","timestamp":1651060618000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Individuality Meets Commonality: A Unified Graph Learning Framework for 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":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5922-9358","authenticated-orcid":false,"given":"Songhe","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"issue":"9","key":"e_1_3_2_2_2","first-page":"820","article-title":"Solution of the matrix equation AX + XB = C [F4]","volume":"15","author":"Bartels R. 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