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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>Ensemble clustering (EC), utilizing multiple basic partitions (BPs) to yield a robust consensus clustering, has shown promising clustering performance. Nevertheless, most current algorithms suffer from two challenging hurdles: (1) a surge of EC-based methods only focus on pair-wise sample correlation while fully ignoring the high-order correlations of diverse views. (2) they deal directly with the co-association (CA) matrices generated from BPs, which are inevitably corrupted by noise and thus degrade the clustering performance. To address these issues, we propose a novel Double High-Order Correlation Preserved Robust Multi-View Ensemble Clustering (DC-RMEC) method, which preserves the high-order inter-view correlation and the high-order correlation of original data simultaneously. Specifically, DC-RMEC constructs a hypergraph from BPs to fuse high-level complementary information from different algorithms and incorporates multiple CA-based representations into a low-rank tensor to discover the high-order relevance underlying CA matrices, such that double high-order correlation of multi-view features could be dexterously uncovered. Moreover, a marginalized denoiser is invoked to gain robust view-specific CA matrices. Furthermore, we develop a unified framework to jointly optimize the representation tensor and the result matrix. An effective iterative optimization algorithm is designed to optimize our DC-RMEC model by resorting to the alternating direction method of multipliers. Extensive experiments on seven real-world multi-view datasets have demonstrated the superiority of DC-RMEC compared with several state-of-the-art multi-view ensemble clustering methods.<\/jats:p>","DOI":"10.1145\/3612923","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T12:18:39Z","timestamp":1691065119000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Double High-Order Correlation Preserved Robust Multi-View Ensemble Clustering"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4203-2743","authenticated-orcid":false,"given":"Xiaojia","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9589-1349","authenticated-orcid":false,"given":"Tingting","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3564-6042","authenticated-orcid":false,"given":"Qiangqiang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen), China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3540-5775","authenticated-orcid":false,"given":"Youfa","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1970-1993","authenticated-orcid":false,"given":"Yongyong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3216-7027","authenticated-orcid":false,"given":"Jingyong","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.08.024"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02310791"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354948"},{"key":"e_1_3_2_5_2","first-page":"767","article-title":"Marginalized denoising autoencoders for domain adaptation","author":"Chen Minmin","year":"2012","unstructured":"Minmin Chen, Zhixiang Xu, Kilian Weinberger, and Fei Sha. 2012. 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