{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T18:53:24Z","timestamp":1776106404855,"version":"3.50.1"},"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":[[2019,8]]},"abstract":"<jats:p>Multi-view clustering aims to leverage information from multiple views to improve clustering. Most previous works assumed that each view has complete data. However, in real-world datasets, it is often the case that a view may contain some missing data, resulting in the incomplete multi-view clustering problem. Previous methods for this problem have at least one of the following drawbacks: (1) employing shallow models, which cannot well handle the dependence and discrepancy among different views; (2) ignoring the hidden information of the missing data; (3) dedicated to the two-view case. To eliminate all these drawbacks, in this work we present an Adversarial Incomplete Multi-view Clustering (AIMC) method. Unlike most existing methods which only learn a new representation with existing views, AIMC seeks the common latent space of multi-view data and performs missing data inference simultaneously. In particular, the element-wise reconstruction and the generative adversarial network (GAN) are integrated to infer the missing data. They aim to capture overall structure and get a deeper semantic understanding respectively. Moreover, an aligned clustering loss is designed to obtain a better clustering structure. Experiments conducted on three datasets show that AIMC performs well and outperforms baseline methods.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/546","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"3933-3939","source":"Crossref","is-referenced-by-count":115,"title":["Adversarial Incomplete Multi-view Clustering"],"prefix":"10.24963","author":[{"given":"Cai","family":"Xu","sequence":"first","affiliation":[{"name":"State Key Lab of ISN, School of Computer Science and Technology, Xidian University"}]},{"given":"Ziyu","family":"Guan","sequence":"additional","affiliation":[{"name":"State Key Lab of ISN, School of Computer Science and Technology, Xidian University"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Lab of ISN, School of Computer Science and Technology, Xidian University"}]},{"given":"Hongchang","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Lab of ISN, School of Computer Science and Technology, Xidian University"}]},{"given":"Yunfei","family":"Niu","sequence":"additional","affiliation":[{"name":"State Key Lab of ISN, School of Computer Science and Technology, Xidian University"}]},{"given":"Beilei","family":"Ling","sequence":"additional","affiliation":[{"name":"State Key Lab of ISN, School of Computer Science and Technology, Xidian University"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:50:02Z","timestamp":1564285802000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/546"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/546","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}