{"type":"ScholarlyArticle","title":"Deep Adversarial Multi-view Clustering Network","authors":[{"name":"Zhaoyang Li","slug":"zhaoyang-li","given":"Zhaoyang","family":"Li","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an 710071 , China."},{"name":"Qianqian Wang","slug":"qianqian-wang","given":"Qianqian","family":"Wang","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an 710071 , China."},{"name":"Zhiqiang Tao","slug":"zhiqiang-tao","given":"Zhiqiang","family":"Tao","affiliation":"Department of Electrical and Computer Engineering , Northeastern University , USA."},{"name":"Quanxue Gao","slug":"quanxue-gao","given":"Quanxue","family":"Gao","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an 710071 , China."},{"name":"Zhaohua Yang","slug":"zhaohua-yang","given":"Zhaohua","family":"Yang","affiliation":"School of Instrumentation Science and Opto-electronics Engineering , Beihang University , China."}],"abstract":"Multi-view clustering has attracted increasing attention in recent years by exploiting common clustering structure across multiple views. Most existing multi-view clustering algorithms use shallow and linear embedding functions to learn the common structure of multi-view data. However, these methods cannot fully utilize the non-linear property of multi-view data, which is important to reveal complex cluster structure underlying multi-view data. In this paper, we propose a novel multi-view clustering method, named Deep Adversarial Multi-view Clustering (DAMC) network, to learn the intrinsic structure embedded in multi-view data. Specifically, our model adopts deep auto-encoders to learn latent representations shared by multiple views, and meanwhile leverages adversarial training to further capture the data distribution and disentangle the latent space. Experimental results on several real-world datasets demonstrate that the proposed method outperforms the state-of art methods.","doi":"10.24963/ijcai.2019/409","url":"https://ijcai.org/proceedings/2019/409","pdf":"https://ijcai.org/proceedings/2019/0409.pdf","markdown":"https://ijcai.org/proceedings/2019/409.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/409","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 A-L","track":"Main track","number":409,"pages":{"first":2952,"last":2958},"keywords":["Machine Learning: Data Mining","Machine Learning: Adversarial Machine Learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Abavisani and Patel, 2018] Mahdi Abavisani and Vishal M Patel. 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