{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:43:06Z","timestamp":1777704186515,"version":"3.51.4"},"reference-count":42,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,3,4]]},"abstract":"<jats:p>\u00a0Multi-view clustering algorithms mostly apply to data without incomplete instances. However, in real-world applications, representations for the same instance are probably absent from several but not all views. This incompleteness disables traditional multi-view clustering methods from grouping incomplete multi-view data. Recently, multi-view clustering methods on incomplete data have been proposed, and the existing methods have two limitations. One is that most methods were developed for incomplete datasets only with two views. The other is that most methods were incapable of grouping data with complex distributions. In this paper, we propose a novel incomplete multi-view clustering algorithm named IMSVC, in which we adopt spectral analysis to supervise the common representation extracted from all the views. Firstly, IMVSC constructs a bipartite graph for each view. By introducing an instance-view indicator matrix to indicate whether a representation exists in a view or not, we calculate the edge weights of bipartite graph based on the point-to-point similarity. Secondly, IMVSC constructs the multi-view relationship by guiding the multiple views to share the same instance partitioning. Finally, we create a novel iterative method to optimize IMVSC. Experimental results show sound performance of the proposed algorithm on several incomplete datasets.<\/jats:p>","DOI":"10.3233\/jifs-190380","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T12:32:44Z","timestamp":1579609964000},"page":"2991-3001","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Incomplete multi-view spectral clustering"],"prefix":"10.1177","volume":"38","author":[{"given":"Qianli","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Zong","sequence":"additional","affiliation":[{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianchao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyue","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,1,20]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","article-title":"Non-negative matrix factorization in multimodality data for segmentation and label prediction","author":"Akata Z.","year":"2011","unstructured":"AkataZ., ThurauC. and BauckhageC., Non-negative matrix factorization in multimodality data for segmentation and label prediction, In Computer Vision Winter Workshop (2011), pages 1\u20138.","journal-title":"Computer Vision Winter Workshop"},{"key":"e_1_3_2_3_2","first-page":"19","article-title":"Multi-View Clustering","author":"Bickel S.","year":"2004","unstructured":"BickelS. and SchefferT., Multi-View Clustering, In IEEE International Conference on Data Mining (2004), pages 19\u201326.","journal-title":"IEEE International Conference on Data Mining"},{"key":"e_1_3_2_4_2","first-page":"2598","article-title":"Multi-view k-means clustering on big data","author":"Cai X.","year":"2013","unstructured":"CaiX., NieF. and HuangH., Multi-view k-means clustering on big data, In International Joint Conferences on Artificial Intelligence (2013), pages 2598\u20132604.","journal-title":"International Joint Conferences on Artificial Intelligence"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.053"},{"key":"e_1_3_2_6_2","first-page":"129","article-title":"Multi-view clustering via canonical correlation analysis","author":"Chaudhuri K.","year":"2009","unstructured":"ChaudhuriK., KakadeS.M., LivescuK. and SridharanK., Multi-view clustering via canonical correlation analysis, In International Conference on Machine Learning (2009), pages 129\u2013136.","journal-title":"International Conference on Machine Learning"},{"key":"e_1_3_2_7_2","first-page":"320","article-title":"Flexible and robust co-regularized multidomain graph clustering","author":"Cheng W.","year":"2013","unstructured":"ChengW., ZhangX., GuoZ., WuY., SullivanP.F. and WangW., Flexible and robust co-regularized multidomain graph clustering, In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2013), pages 320\u2013328.","journal-title":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining"},{"key":"e_1_3_2_8_2","article-title":"Spectral clustering with two views","author":"de Sa V.R.","year":"2005","unstructured":"de SaV.R., Spectral clustering with two views, In ICML workshop on learning with multiple views, (2005).","journal-title":"ICML workshop on learning with multiple views,"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","unstructured":"DhillonI.S. 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