{"type":"ScholarlyArticle","title":"Adversarial Incomplete Multi-view Clustering","authors":[{"name":"Cai Xu","slug":"cai-xu","given":"Cai","family":"Xu","affiliation":"State Key Lab of ISN , School of Computer Science and Technology , Xidian University"},{"name":"Ziyu Guan","slug":"ziyu-guan","given":"Ziyu","family":"Guan","affiliation":"State Key Lab of ISN , School of Computer Science and Technology , Xidian University"},{"name":"Wei Zhao","slug":"wei-zhao","given":"Wei","family":"Zhao","affiliation":"State Key Lab of ISN , School of Computer Science and Technology , Xidian University"},{"name":"Hongchang Wu","slug":"hongchang-wu","given":"Hongchang","family":"Wu","affiliation":"State Key Lab of ISN , School of Computer Science and Technology , Xidian University"},{"name":"Yunfei Niu","slug":"yunfei-niu","given":"Yunfei","family":"Niu","affiliation":"State Key Lab of ISN , School of Computer Science and Technology , Xidian University"},{"name":"Beilei Ling","slug":"beilei-ling","given":"Beilei","family":"Ling","affiliation":"State Key Lab of ISN , School of Computer Science and Technology , Xidian University"}],"abstract":"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.","doi":"10.24963/ijcai.2019/546","url":"https://ijcai.org/proceedings/2019/546","pdf":"https://ijcai.org/proceedings/2019/0546.pdf","markdown":"https://ijcai.org/proceedings/2019/546.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/546","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":546,"pages":{"first":3933,"last":3939},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Clustering","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Amini et al., 2009] Massih Amini, Nicolas Usunier, and Cyril Goutte. 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