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However, real\u2010world data usually suffers from missing some samples in each view and has a small number of labeled samples. Additionally, almost all existing multi\u2010view clustering models do not execute incomplete multi\u2010view data well and fail to fully utilize the labeled samples to reduce computational complexity, which precludes them from practical application. In view of these problems, this paper proposes a novel framework called Semi\u2010supervised Multi\u2010View Clustering with Weighted Anchor Graph Embedding (SMVC_WAGE), which is conceptually simple and efficiently generates high\u2010quality clustering results in practice. Specifically, we introduce a simple and effective anchor strategy. Based on selected anchor points, we can exploit the intrinsic and extrinsic view information to bridge all samples and capture more reliable nonlinear relations, which greatly enhances efficiency and improves stableness. Meanwhile, we construct the global fused graph compatibly across multiple views via a parameter\u2010free graph fusion mechanism which directly coalesces the view\u2010wise graphs. To this end, the proposed method can not only deal with complete multi\u2010view clustering well but also be easily extended to incomplete multi\u2010view cases. Experimental results clearly show that our algorithm surpasses some state\u2010of\u2010the\u2010art competitors in clustering ability and time cost.<\/jats:p>","DOI":"10.1155\/2021\/4296247","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T20:05:09Z","timestamp":1627416309000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Semi\u2010Supervised Multi\u2010View Clustering with Weighted Anchor Graph Embedding"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7062-3233","authenticated-orcid":false,"given":"Senhong","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5156-3538","authenticated-orcid":false,"given":"Jiangzhong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2059-8818","authenticated-orcid":false,"given":"Fangyuan","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7561-3704","authenticated-orcid":false,"given":"Qingyun","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1625-2168","authenticated-orcid":false,"given":"Shangsong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0633-7224","authenticated-orcid":false,"given":"Bingo","family":"Wing-Kuen Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,7,27]]},"reference":[{"key":"e_1_2_9_1_2","unstructured":"YuanX. 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