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Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            As a significant extension of classical clustering methods, ensemble clustering first generates multiple basic clusterings and then fuses them into one consensus partition by solving a problem concerning graph partition with respect to the co-association matrix. Although the collaborative cluster structure among basic clusterings can be well discovered by ensemble clustering, most advanced ensemble clustering utilizes the self-representation strategy with the constraint of low-rank to explore a shared consensus representation matrix in multiple views. However, they still encounter two challenges: (1)\n            <jats:bold>\n              <jats:italic>high computational cost<\/jats:italic>\n            <\/jats:bold>\n            caused by both the matrix inversion operation and singular value decomposition of large-scale square matrices; (2)\n            <jats:bold>\n              <jats:italic>less considerable attention on high-order correlation<\/jats:italic>\n            <\/jats:bold>\n            attributed to the pursue of the two-dimensional pair-wise relationship matrix. In this article, based on low-rank and sparse decomposition from both matrix and tensor perspectives, we propose two novel multi-view ensemble clustering methods, which tangibly decrease computational complexity. Specifically, our first method utilizes low-rank and sparse matrix decomposition to learn one common co-association matrix, while our last method constructs all co-association matrices into one third-order tensor to investigate the high-order correlation among multiple views by low-rank and sparse tensor decomposition. We adopt the alternating direction method of multipliers to solve two convex models by dividing them into several subproblems with closed-form solution. Experimental results on ten real-world datasets prove the effectiveness and efficiency of the proposed two multi-view ensemble clustering methods by comparing them with other advanced ensemble clustering methods.\n          <\/jats:p>","DOI":"10.1145\/3589768","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T12:21:57Z","timestamp":1680178917000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-view Ensemble Clustering via Low-rank and Sparse Decomposition: From Matrix to Tensor"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9300-5958","authenticated-orcid":false,"given":"Xuanqi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), 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), Shenzhen, 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), Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0952-8325","authenticated-orcid":false,"given":"Guokai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3529-0541","authenticated-orcid":false,"given":"Zhongyun","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), 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), Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-017-0756-7"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.5555\/2994445"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2018.2879245"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.5555\/1958473.1958487"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2010.5707106"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2706326"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3068646"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2941319"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.5555\/2736765.2736882"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944973"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015414"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.113"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2021.3064051"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3417337"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1137\/110837711"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00131"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2968750"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.88"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3182384"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783287"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2650229"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2891760"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11617"},{"key":"e_1_3_2_26_2","first-page":"849","volume-title":"Advances in Neural Information Processing Systems","author":"Ng Andrew Y.","year":"2002","unstructured":"Andrew Y. 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