{"type":"ScholarlyArticle","title":"Weakly Supervised Multi-Label Learning via Label Enhancement","authors":[{"name":"JiaQi Lv","slug":"jiaqi-lv","given":"JiaQi","family":"Lv","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , China School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"},{"name":"Ning Xu","slug":"ning-xu","given":"Ning","family":"Xu","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , China School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"},{"name":"RenYi Zheng","slug":"renyi-zheng","given":"RenYi","family":"Zheng","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , China School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"},{"name":"Xin Geng","slug":"xin-geng","given":"Xin","family":"Geng","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , China School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"}],"abstract":"Weakly supervised multi-label learning (WSML) concentrates on a more challenging multi-label classification problem, where some labels in the training set are missing. Existing approaches make multi-label prediction by exploiting the incomplete logical labels directly without considering the relative importance of each label to an instance. In this paper, a novel two-stage strategy named Weakly Supervised Multi-label Learning via Label Enhancement (WSMLLE) is proposed to learn from weakly supervised data via label enhancement. Firstly, the relative importance of each label, i.e., the description degrees are recovered by leveraging the structural information in the feature space and local correlations learned from the label space. Then, a tailored multi-label predictive model is induced by learning from the training instances with the recovered description degrees. To our best knowledge, it is the first attempt to unify the complement of the missing labels and the recovery of the description degrees into the same framework. Extensive experiments across a wide range of real-world datasets clearly validate the superiority of the proposed approach.","doi":"10.24963/ijcai.2019/430","url":"https://ijcai.org/proceedings/2019/430","pdf":"https://ijcai.org/proceedings/2019/0430.pdf","markdown":"https://ijcai.org/proceedings/2019/430.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/430","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":430,"pages":{"first":3101,"last":3107},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Cour et al., 2011] Timothee Cour, Ben Sapp, and Ben Taskar. 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