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However, most existing clustering methods still have the following shortcomings: (a) It has no meaning in practical applications for singular values to be treated equally. (b) They often ignore that data samples in the real world usually exist in multiple nonlinear subspaces. In order to solve the above shortcomings, we propose a hyper-Laplacian regularized multi-view subspace clustering model that joints representation learning and weighted tensor nuclear norm constraint, namely JWHMSC. Specifically, in the JWHMSC model, firstly, in order to capture the global structure between different views, the subspace representation matrices of all views are stacked into a low-rank constrained tensor. Secondly, hyper-Laplace graph regularization is adopted to preserve the local geometric structure embedded in the high-dimensional ambient space. Thirdly, considering the prior information of singular values, the weighted tensor nuclear norm (WTNN) based on t-SVD is introduced to treat singular values differently, which makes the JWHMSC more accurately obtain the sample distribution of classification information. Finally, representation learning, WTNN constraint and hyper-Laplacian graph regularization constraint are integrated into a framework to obtain the overall optimal solution of the algorithm. Compared with the state-of-the-art method, the experimental results on eight benchmark datasets show the good performance of the proposed method JWHMSC in multi-view clustering.<\/jats:p>","DOI":"10.3233\/jifs-212316","type":"journal-article","created":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T08:42:52Z","timestamp":1640940172000},"page":"5809-5822","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Hyper-Laplacian regularized multi-view subspace clustering with jointing representation learning and weighted tensor\u00a0nuclear norm constraint"],"prefix":"10.1177","volume":"42","author":[{"given":"Qingjiang","family":"Xiao","sequence":"first","affiliation":[{"name":"Northwest Minzu University","place":["China"]}]},{"given":"Shiqiang","family":"Du","sequence":"additional","affiliation":[{"name":"Northwest Minzu University","place":["China"]},{"name":"Northwest Minzu University","place":["China"]}]},{"given":"Yao","family":"Yu","sequence":"additional","affiliation":[{"name":"Northwest Minzu University","place":["China"]}]},{"given":"Yixuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Northwest Minzu University","place":["China"]}]},{"given":"Jinmei","family":"Song","sequence":"additional","affiliation":[{"name":"Northwest Minzu University","place":["China"]}]}],"member":"179","published-online":{"date-parts":[[2021,12,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.02.104"},{"key":"e_1_3_2_3_2","first-page":"393","article-title":"A co-training approach for multi-viewspectral clustering","author":"Kumar A.","year":"2011","unstructured":"KumarA., Daum\u00c3l\u2019.H., A co-training approach for multi-viewspectral clustering, Proceedings of the 28th internationalconference on machine learning (ICML-11) (2011), 393\u2013400.","journal-title":"Proceedings of the 28th internationalconference on machine learning (ICML-11)"},{"key":"e_1_3_2_4_2","first-page":"1413","article-title":"Co-regularized multi-view spectral clustering","volume":"24","author":"Kumar A.","year":"2011","unstructured":"KumarA., RaiP., Daume.H., Co-regularized multi-view spectral clustering, Advances in Neural Information Processing Systems 24 (2011), 1413\u20131421.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.08.019"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.10.022"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2903810"},{"key":"e_1_3_2_8_2","unstructured":"LiZ. 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