{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T05:17:03Z","timestamp":1755926223524},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Although multi-view clustering is capable to usemore information than single view clustering, existing multi-view clustering methods still have issues to be addressed, such as initialization sensitivity, the specification of the number of clusters,and the influence of outliers. In this paper, we propose a robust multi-view clustering method to address these issues. Specifically, we first propose amulti-view based sum-of-square error estimation tomake the initialization easy and simple as well asuse a sum-of-norm regularization to automaticallylearn the number of clusters according to data distribution. We further employ robust estimators constructed by the half-quadratic theory to avoid theinfluence of outliers for conducting robust estimations of both sum-of-square error and the numberof clusters. Experimental results on both syntheticand real datasets demonstrate that our method outperforms the state-of-the-art methods.\u00a0\u00a0<\/jats:p>","DOI":"10.24963\/ijcai.2018\/455","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"3278-3284","source":"Crossref","is-referenced-by-count":5,"title":["Robust Multi-view Learning via Half-quadratic Minimization"],"prefix":"10.24963","author":[{"given":"Yonghua","family":"Zhu","sequence":"first","affiliation":[{"name":"Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University"},{"name":"Guangxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:52:58Z","timestamp":1530769978000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/455"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/455","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}