{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T16:01:33Z","timestamp":1781798493939,"version":"3.54.5"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T00:00:00Z","timestamp":1650240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T00:00:00Z","timestamp":1650240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s11432-020-3369-8","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T13:05:18Z","timestamp":1650891918000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Incomplete multi-view clustering via local and global co-regularization"],"prefix":"10.1007","volume":"65","author":[{"given":"Jiye","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyuan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dianhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,18]]},"reference":[{"key":"3369_CR1","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U von Luxburg","year":"2007","unstructured":"von Luxburg U. A tutorial on spectral clustering. Stat Comput, 2007, 17: 395\u2013416","journal-title":"Stat Comput"},{"key":"3369_CR2","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez A, Laio A. Clustering by fast search and find of density peaks. Science, 2014, 344: 1492\u20131496","journal-title":"Science"},{"key":"3369_CR3","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1007\/s11432-010-3112-z","volume":"53","author":"S H Yue","year":"2010","unstructured":"Yue S H, Wang J S, Tao G, et al. An unsupervised grid-based approach for clustering analysis. Sci China Inf Sci, 2010, 53: 1345\u20131357","journal-title":"Sci China Inf Sci"},{"key":"3369_CR4","first-page":"012109","volume":"57","author":"C Z Li","year":"2014","unstructured":"Li C Z, Xu Z B, Qiao C, et al. Hierarchical clustering driven by cognitive features. Sci China Inf Sci, 2014, 57: 012109","journal-title":"Sci China Inf Sci"},{"key":"3369_CR5","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.1109\/TPAMI.2017.2739147","volume":"40","author":"Z W Li","year":"2018","unstructured":"Li Z W, Cheong L F, Yang S G, et al. Simultaneous clustering and model selection: algorithm, theory and applications. IEEE Trans Pattern Anal Mach Intell, 2018, 40: 1964\u20131978","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3369_CR6","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1109\/TCBB.2017.2743711","volume":"16","author":"Y S Wang","year":"2019","unstructured":"Wang Y S, Fang H Y, Yang D J, et al. Network clustering analysis using mixture exponential-family random graph models and its application in genetic interaction data. IEEE ACM Trans Comput Biol Bioinf, 2019, 16: 1743\u20131752","journal-title":"IEEE ACM Trans Comput Biol Bioinf"},{"key":"3369_CR7","doi-asserted-by":"crossref","unstructured":"Oyelade J, Isewon I, Oladipupo O, et al. Data clustering: algorithms and its applications. In: Proceedings of the 19th International Conference on Computational Science and Its Applications (ICCSA), Saint Petersburg, 2019. 71\u201381","DOI":"10.1109\/ICCSA.2019.000-1"},{"key":"3369_CR8","doi-asserted-by":"publisher","first-page":"103101","DOI":"10.1007\/s11432-017-9178-8","volume":"60","author":"J L Wang","year":"2017","unstructured":"Wang J L, Lu Y H, Liu J B, et al. A robust three-stage approach to large-scale urban scene recognition. Sci China Inf Sci, 2017, 60: 103101","journal-title":"Sci China Inf Sci"},{"key":"3369_CR9","doi-asserted-by":"publisher","first-page":"3247","DOI":"10.1109\/TPAMI.2020.2979699","volume":"43","author":"L Bai","year":"2021","unstructured":"Bai L, Liang J Y, Cao F Y. Semi-supervised clustering with constraints of different types from multiple information sources. IEEE Trans Pattern Anal Mach Intell, 2021, 43: 3247\u20133258","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3369_CR10","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TPAMI.2018.2877660","volume":"42","author":"C Q Zhang","year":"2020","unstructured":"Zhang C Q, Fu H Z, Hu Q H, et al. Generalized latent multi-view subspace clustering. IEEE Trans Pattern Anal Mach Intell, 2020, 42: 86\u201399","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3369_CR11","doi-asserted-by":"crossref","unstructured":"Liu J, Cao F Y, Gao X Z, et al. A cluster-weighted kernel k-means method for multi-view clustering. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), New York, 2020. 4860\u20134867","DOI":"10.1609\/aaai.v34i04.5922"},{"key":"3369_CR12","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TKDE.2019.2903810","volume":"32","author":"H Wang","year":"2020","unstructured":"Wang H, Yang Y, Liu B. GMC: graph-based multi-view clustering. IEEE Trans Knowl Data Eng, 2020, 32: 1116\u20131129","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3369_CR13","doi-asserted-by":"publisher","first-page":"052110","DOI":"10.1007\/s11432-013-4832-7","volume":"56","author":"Z M Lu","year":"2013","unstructured":"Lu Z M, Liu C, Zhang Q, et al. Visual analytics for the clustering capability of data. Sci China Inf Sci, 2013, 56: 052110","journal-title":"Sci China Inf Sci"},{"key":"3369_CR14","doi-asserted-by":"crossref","unstructured":"Xu X M, Li K K, Xu C, et al. GDFace: gated deformation for multi-view face image synthesis. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), New York, 2020. 12532\u201312540","DOI":"10.1609\/aaai.v34i07.6942"},{"key":"3369_CR15","doi-asserted-by":"crossref","unstructured":"Fei H L, Li P. Cross-lingual unsupervised sentiment classification with multi-view transfer learning. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2020. 5759\u20135771","DOI":"10.18653\/v1\/2020.acl-main.510"},{"key":"3369_CR16","doi-asserted-by":"publisher","first-page":"070104","DOI":"10.1007\/s11432-016-5587-8","volume":"59","author":"X P Jiang","year":"2016","unstructured":"Jiang X P, Hu X H, He T T. Identification of the clustering structure in microbiome data by density clustering on the Manhattan distance. Sci China Inf Sci, 2016, 59: 070104","journal-title":"Sci China Inf Sci"},{"key":"3369_CR17","doi-asserted-by":"crossref","unstructured":"Wang M H, Lin Y J, Yang K P, et at. M2GRL: a multi-task multi-view graph representation learning framework for web-scale recommender systems. In: Proceedings of the 26th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), 2020. 2349\u20132358","DOI":"10.1145\/3394486.3403284"},{"key":"3369_CR18","doi-asserted-by":"publisher","first-page":"012201","DOI":"10.1007\/s11432-015-5429-0","volume":"59","author":"X N Zhang","year":"2016","unstructured":"Zhang X N, Song S J, Zhu L, et al. Unsupervised learning of Dirichlet process mixture models with missing data. Sci China Inf Sci, 2016, 59: 012201","journal-title":"Sci China Inf Sci"},{"key":"3369_CR19","doi-asserted-by":"publisher","unstructured":"Zhang C Q, Cui Y J, Han Z B, et al. Deep partial multi-view learning. IEEE Trans Pattern Anal Mach Intell, 2020. doi: https:\/\/doi.org\/10.1109\/TPAMI.2020.3037734","DOI":"10.1109\/TPAMI.2020.3037734"},{"key":"3369_CR20","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1007\/s00521-013-1362-6","volume":"23","author":"S L Sun","year":"2013","unstructured":"Sun S L. A survey of multi-view machine learning. Neural Comput Appl, 2013, 23: 2031\u20132038","journal-title":"Neural Comput Appl"},{"key":"3369_CR21","doi-asserted-by":"publisher","first-page":"83","DOI":"10.26599\/BDMA.2018.9020003","volume":"1","author":"Y Yang","year":"2018","unstructured":"Yang Y, Wang H. Multi-view clustering: a survey. Big Data Min Anal, 2018, 1: 83\u2013107","journal-title":"Big Data Min Anal"},{"key":"3369_CR22","doi-asserted-by":"crossref","unstructured":"Li S Y, Jiang Y, Zhou Z H. Partial multi-view clustering. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), Qu\u00e9bec, 2014. 1968\u20131974","DOI":"10.1609\/aaai.v28i1.8973"},{"key":"3369_CR23","unstructured":"Zhao H D, Liu H F, Fu Y. Incomplete multi-modal visual data grouping. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), New York, 2016. 2392\u20132398"},{"key":"3369_CR24","doi-asserted-by":"crossref","unstructured":"Shao W X, He L f, Philip S Y. Multiple incomplete views clustering via weighted nonnegative matrix factorization with L2,1 regularization. In: Proceedigns of Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML & PKDD), Porto, 2015. 318\u2013334","DOI":"10.1007\/978-3-319-23528-8_20"},{"key":"3369_CR25","doi-asserted-by":"crossref","unstructured":"Zhou W, Wang H, Yang Y. Consensus graph learning for incomplete multi-view clustering. In: Proceedings of the 23rd Pacific-Asia Advances in Knowledge Discovery and Data Mining Conference (PAKDD), Macau, 2019. 529\u2013540","DOI":"10.1007\/978-3-030-16148-4_41"},{"key":"3369_CR26","doi-asserted-by":"crossref","unstructured":"Min C, Cheng M M, Yu J, et al. Partial multi-view clustering via auto-weighting similarity completion. In: Proceedings of the 13th Chinese Conference on Biometric Recognition (CCBR), Urumqi, 2018. 214\u2013222","DOI":"10.1007\/978-3-319-97909-0_23"},{"key":"3369_CR27","doi-asserted-by":"crossref","unstructured":"Guo J, Ye J H. Anchors bring ease: an embarrassingly simple approach to partial multi-view clustering. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), Hawaii, 2019. 118\u2013125","DOI":"10.1609\/aaai.v33i01.3301118"},{"key":"3369_CR28","doi-asserted-by":"crossref","unstructured":"Wu J, Zhuge W Z, Tao H, et al. Incomplete multi-view clustering via structured graph learning. In: Proceeding of the 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI), Nanjing, 2018. 98\u2013112","DOI":"10.1007\/978-3-319-97304-3_8"},{"key":"3369_CR29","doi-asserted-by":"publisher","first-page":"2780","DOI":"10.1109\/TIP.2019.2952696","volume":"29","author":"L Yang","year":"2020","unstructured":"Yang L, Shen C Y, Hu Q H, et al. Adaptive sample-level graph combination for partial multiview clustering. IEEE Trans Image Process, 2020, 29: 2780\u20132794","journal-title":"IEEE Trans Image Process"},{"key":"3369_CR30","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1109\/TMM.2020.3013408","volume":"23","author":"J Wen","year":"2021","unstructured":"Wen J, Yan K, Zhang Z, et al. Adaptive graph completion based incomplete multi-view clustering. IEEE Trans Multimedia, 2021, 23: 2493\u20132504","journal-title":"IEEE Trans Multimedia"},{"key":"3369_CR31","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/TCYB.2020.2987164","volume":"51","author":"J Wen","year":"2021","unstructured":"Wen J, Zhang Z, Zhang Z, et al. Generalized incomplete multiview clustering with flexible locality structure diffusion. IEEE Trans Cybern, 2021, 51: 101\u2013114","journal-title":"IEEE Trans Cybern"},{"key":"3369_CR32","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1109\/TCYB.2018.2884715","volume":"50","author":"J Wen","year":"2020","unstructured":"Wen J, Xu Y, Liu H. Incomplete multiview spectral clustering with adaptive graph learning. IEEE Trans Cybern, 2020, 50: 1418\u20131429","journal-title":"IEEE Trans Cybern"},{"key":"3369_CR33","doi-asserted-by":"crossref","unstructured":"Wen J, Zhang Z, Zhang Z, et al. Unified tensor framework for incomplete multi-view clustering and missing-view Inferring. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021","DOI":"10.1609\/aaai.v35i11.17231"},{"key":"3369_CR34","doi-asserted-by":"crossref","unstructured":"Liu X W, Zhu X Z, Li M M, et al. Multiple kernel k-means with incomplete kernels. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), San Francisco, 2017. 2259\u20132265","DOI":"10.1609\/aaai.v31i1.10893"},{"key":"3369_CR35","doi-asserted-by":"crossref","unstructured":"Zhu X Z, Liu X W, Li M M, et al. Localized incomplete multiple kernel k-means. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, 2018. 3271\u20133277","DOI":"10.24963\/ijcai.2018\/454"},{"key":"3369_CR36","doi-asserted-by":"publisher","first-page":"2410","DOI":"10.1109\/TPAMI.2018.2879108","volume":"41","author":"X W Liu","year":"2019","unstructured":"Liu X W, Zhu X Z, Li M M, et al. Late fusion incomplete multi-view clustering. IEEE Trans Pattern Anal Mach Intell, 2019, 41: 2410\u20132423","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3369_CR37","unstructured":"Bai L, Liang J Y. Sparse subspace clustering with entropy-norm. In: Proceedings of the 37th International Conference on Machine Learning (ICML), Vienna, 2020. 561\u2013568"},{"key":"3369_CR38","doi-asserted-by":"crossref","unstructured":"Nie F P, Wang X Q, Huang H. Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 20th ACM International Conference on Knowledge Discovery & Data Mining (SIGKDD), New York, 2014. 977\u2013986","DOI":"10.1145\/2623330.2623726"},{"key":"3369_CR39","doi-asserted-by":"crossref","unstructured":"Wen J, Zhang X, Xu Y, et al. Incomplete multi-view clustering via graph regularized matrix factorization. In: Proceedings of European Conference on Computer Vision (ECCV), Munich, 2018. 593\u2013608","DOI":"10.1007\/978-3-030-11018-5_47"},{"key":"3369_CR40","doi-asserted-by":"crossref","unstructured":"Hu M L, Chen S C. Doubly aligned incomplete multi-view clustering. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, 2018. 2262\u20132268","DOI":"10.24963\/ijcai.2018\/313"},{"key":"3369_CR41","first-page":"2579","volume":"9","author":"L V D Maaten","year":"2008","unstructured":"Maaten L V D, Hinton G. Visualizing data using t-SNE. J Mach Learn Res, 2008, 9: 2579\u20132605","journal-title":"J Mach Learn Res"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-3369-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-020-3369-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-3369-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T20:25:30Z","timestamp":1685910330000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-020-3369-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,18]]},"references-count":41,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["3369"],"URL":"https:\/\/doi.org\/10.1007\/s11432-020-3369-8","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,18]]},"assertion":[{"value":"7 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"152105"}}