{"type":"ScholarlyArticle","title":"Multi-View Multi-Label Learning with View-Specific Information Extraction","authors":[{"name":"Xuan Wu","slug":"xuan-wu","given":"Xuan","family":"Wu","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Alibaba Group , Hangzhou , China"},{"name":"Qing-Guo Chen","slug":"qing-guo-chen","given":"Qing-Guo","family":"Chen","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Yao Hu","slug":"yao-hu","given":"Yao","family":"Hu","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Dengbao Wang","slug":"dengbao-wang","given":"Dengbao","family":"Wang","affiliation":"College of Computer and Information Science , Southwest University , Chongqing 400715 , China"},{"name":"Xiaodong Chang","slug":"xiaodong-chang","given":"Xiaodong","family":"Chang","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Xiaobo Wang","slug":"xiaobo-wang","given":"Xiaobo","family":"Wang","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Min-Ling Zhang","slug":"min-ling-zhang","given":"Min-Ling","family":"Zhang","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Laboratory of Computer Network and Information Integration (Southeast University) , Ministry of Education , China"}],"abstract":"Multi-view multi-label learning serves an important framework to learn from objects with diverse representations and rich semantics. Existing multi-view multi-label learning techniques focus on exploiting shared subspace for fusing multi-view representations, where helpful view-specific information for discriminative modeling is usually ignored. In this paper, a novel multi-view multi-label learning approach named SIMM is proposed which leverages shared subspace exploitation and view-specific information extraction. For shared subspace exploitation, SIMM jointly minimizes confusion adversarial loss and multi-label loss to utilize shared information from all views. For view-specific information extraction, SIMM enforces an orthogonal constraint w.r.t. the shared subspace to utilize view-specific discriminative information. Extensive experiments on real-world data sets clearly show the favorable performance of SIMM against other state-of-the-art multi-view multi-label learning approaches.","doi":"10.24963/ijcai.2019/539","url":"https://ijcai.org/proceedings/2019/539","pdf":"https://ijcai.org/proceedings/2019/0539.pdf","markdown":"https://ijcai.org/proceedings/2019/539.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/539","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":539,"pages":{"first":3884,"last":3890},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Boutell et al., 2004] Matthew R Boutell, Jiebo Luo, Xipeng Shen, and Christopher M Brown. 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