{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:40:19Z","timestamp":1777696819666,"version":"3.51.4"},"reference-count":34,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2025,2,16]],"date-time":"2025-02-16T00:00:00Z","timestamp":1739664000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62136005"],"award-info":[{"award-number":["62136005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62036013"],"award-info":[{"award-number":["62036013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Data Analysis: An International Journal"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p>Multi-view subspace clustering, which aims to partition a set of multi-source data into a common space, has recently attracted wide attention in the field of data analysis and machine learning. Traditional algorithms may face the problem of high complexity in calculating the self-expression matrix and in turning parameter. This paper proposes a novel multi-view subspace clustering model termed as Low-rank Multi-view Subspace Clustering via Adaptive Weight (LMSCAW). LMSCAW decomposes the self-expression matrix into the product of two low-rank representation matrices and thus can fix the rank of the self-expression matrix of each view to increase the stability of the algorithm. In addition, in order to learn the common representation matrix better, LMSCAW fuses the self-expression matrices among multiple views and implicitly weights each view by the Frobenius norm without additional parameters. Extensive experimental results on multiple benchmark datasets are provided to show the effectiveness of the proposed algorithm and its superior performance over other state-of-the-art methods.<\/jats:p>","DOI":"10.1177\/1088467x251314322","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:20:31Z","timestamp":1739751631000},"page":"1367-1378","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Low-rank multi-view subspace clustering via adaptive weight"],"prefix":"10.1177","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2189-3237","authenticated-orcid":false,"given":"Yuanyuan","family":"Jiao","sequence":"first","affiliation":[{"name":"National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0268-2334","authenticated-orcid":false,"given":"Xiao","family":"Ouyang","sequence":"additional","affiliation":[{"name":"College of Science, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1276-466X","authenticated-orcid":false,"given":"Ruidong","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Science, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenping","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Science, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,2,16]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2021.09.017"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3083072"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.07.089"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108196"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2627806"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2013.12.003"},{"key":"e_1_3_4_8_2","doi-asserted-by":"crossref","unstructured":"Ye Y Liu X Yin J et\u00a0al. Co-regularized kernel k-means for multi-view clustering. In: ICPR 2016 pp.1583\u20131588.","DOI":"10.1109\/ICPR.2016.7899863"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3112230"},{"key":"e_1_3_4_10_2","doi-asserted-by":"crossref","unstructured":"Qiang Q Zhang B Wang F et\u00a0al. Fast multi-view discrete clustering with anchor graphs. In: AAAI 2021 pp.9360\u20139367.","DOI":"10.1609\/aaai.v35i11.17128"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3062692"},{"key":"e_1_3_4_12_2","doi-asserted-by":"crossref","unstructured":"Huang S Liu Y Ren Y et\u00a0al. Learning smooth representation for multi-view subspace clustering. In: ACM MM 2022 pp.3421\u20133429.","DOI":"10.1145\/3503161.3548248"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.12.004"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2018.9020003"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-022-00190-8"},{"key":"e_1_3_4_16_2","doi-asserted-by":"crossref","unstructured":"Wei L Song S. Multi-view subspace clustering via an adaptive consensus graph filter. In: Proceedings of the 2024 international conference on multimedia retrieval ICMR 2024 Phuket Thailand June 10-14 2024 (Gurrin C Kongkachandra R Schoeffmann K et\u00a0al. (eds.)) 2024 pp.776\u2013784. ACM. DOI: 10.1145\/3652583.3658009.","DOI":"10.1145\/3652583.3658009"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3285662"},{"key":"e_1_3_4_18_2","doi-asserted-by":"crossref","unstructured":"Zhang C Fu H Liu S et\u00a0al. Low-rank tensor constrained multiview subspace clustering. In: ICCV 2015 pp.1582\u20131590.","DOI":"10.1109\/ICCV.2015.185"},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.05.019"},{"key":"e_1_3_4_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3081930"},{"key":"e_1_3_4_21_2","doi-asserted-by":"crossref","unstructured":"Luo S Zhang C Zhang W et\u00a0al. Consistent and specific multi-view subspace clustering. In: AAAI Vol. 32 2018.","DOI":"10.1609\/aaai.v32i1.11617"},{"key":"e_1_3_4_22_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0176769"},{"key":"e_1_3_4_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119719"},{"key":"e_1_3_4_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108298"},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2877660"},{"key":"e_1_3_4_26_2","doi-asserted-by":"crossref","unstructured":"Luo S Zhang C Zhang W et\u00a0al. Consistent and specific multi-view subspace clustering. In: AAAI 2018 pp.3730\u20133737.","DOI":"10.1609\/aaai.v32i1.11617"},{"key":"e_1_3_4_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3010631"},{"key":"e_1_3_4_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.88"},{"key":"e_1_3_4_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2915593"},{"key":"e_1_3_4_30_2","unstructured":"Nie F Li J Li X. Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: IJCAI 2016 pp.1881\u20131887."},{"key":"e_1_3_4_31_2","doi-asserted-by":"crossref","unstructured":"Kang Z Zhou W Zhao Z et\u00a0al. Large-scale multi-view subspace clustering in linear time. In: AAAI 2020.","DOI":"10.1609\/aaai.v34i04.5867"},{"key":"e_1_3_4_32_2","unstructured":"Jia Y Salzmann M Darrell T. Factorized latent spaces with structured sparsity. In: NIPS 2010 pp.982\u2013990."},{"key":"e_1_3_4_33_2","unstructured":"Gao J Han J Liu J et\u00a0al. Multi-view clustering via joint nonnegative matrix factorization. In: SDM 2013 pp. 252\u2013260."},{"key":"e_1_3_4_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2009.2039566"},{"key":"e_1_3_4_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107524"}],"container-title":["Intelligent Data Analysis: An International Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1088467X251314322","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/1088467X251314322","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1088467X251314322","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:21:03Z","timestamp":1777454463000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/1088467X251314322"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,16]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1177\/1088467X251314322"],"URL":"https:\/\/doi.org\/10.1177\/1088467x251314322","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,16]]}}}