{"type":"ScholarlyArticle","title":"Common-Individual Semantic Fusion for Multi-View Multi-Label Learning","authors":[{"name":"Gengyu Lyu","slug":"gengyu-lyu","given":"Gengyu","family":"Lyu","affiliation":"Faculty of Information Technology , Beijing University of Technology"},{"name":"Weiqi Kang","slug":"weiqi-kang","given":"Weiqi","family":"Kang","affiliation":"Faculty of Information Technology , Beijing University of Technology"},{"name":"Haobo Wang","slug":"haobo-wang","given":"Haobo","family":"Wang","affiliation":"School of Software Technology , Zhejiang University"},{"name":"Zheng Li","slug":"zheng-li","given":"Zheng","family":"Li","affiliation":"Faculty of Information Technology , Beijing University of Technology"},{"name":"Zhen Yang","slug":"zhen-yang","given":"Zhen","family":"Yang","affiliation":"Faculty of Information Technology , Beijing University of Technology"},{"name":"Songhe Feng","slug":"songhe-feng","given":"Songhe","family":"Feng","affiliation":"School of Computer Science and Technology , Beijing Jiaotong University"}],"abstract":"In Multi-View Multi-Label Learning, each instance is described by several heterogeneous features and associated with multiple valid labels simultaneously. Existing methods mainly focus on leveraging feature-level view fusion to capture a common representation for multi-label classifier induction. In this paper, we take a new perspective and propose a new semantic-level fusion model named Common-Individual Semantic Fusion Multi-View Multi-Label Learning Method (CISF). Different from previous feature-level fusion model, our proposed method directly focuses on semantic-level view fusion and simultaneously take both the common semantic across different views and the individual semantic of each specific view into consideration. Specifically, we first assume each view involves some common semantic labels while owns a few exclusive semantic labels. Then, the common and exclusive semantic labels are separately forced to be consensus and diverse to excavate the consistences and complementarities among different views. Afterwards, we introduce the low-rank and sparse constraint to highlight the label co-occurrence relationship of common semantics and the view-specific expression of individual semantics. We provide theoretical guarantee for the strict convexity of our method by properly setting parameters. Extensive experiments on various data sets have verified the superiority of our method.","doi":"10.24963/ijcai.2024/521","url":"https://ijcai.org/proceedings/2024/521","pdf":"https://ijcai.org/proceedings/2024/0521.pdf","markdown":"https://ijcai.org/proceedings/2024/521.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/521","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":521,"pages":{"first":4715,"last":4723},"keywords":["Machine Learning: ML: Multi-view learning","Machine Learning: ML: Weakly supervised learning","Machine Learning: ML: Multi-label learning","Machine Learning: ML: Classification"],"videos":[],"references":[{"position":1,"raw":"[Andrew et al., 2013] G. 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