{"type":"ScholarlyArticle","title":"Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps","authors":[{"name":"Jun-Xiang Mao","slug":"jun-xiang-mao","given":"Jun-Xiang","family":"Mao","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"},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":"The University of Tokyo , Japan"},{"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":"In multi-label classification, it is critical to capitalize on complicated data structures and semantic relationships. Metric learning serves as an effective strategy to provide a better measurement of distances between examples. Existing works on metric learning for multi-label classification mainly learn one single global metric that characterizes latent semantic similarity between multi-label instances. However, such single-semantics metric exploitation approaches can not capture the intrinsic properties of multi-label data possessed of rich semantics. In this paper, the first attempt towards multi-semantics metric learning for multi-label classification is investigated. Specifically, the proposed LIMIC approach simultaneously learns one global and multiple label-specific local metrics by exploiting label-specific side information. The global metric is learned to capture the commonality across all the labels and label-specific local metrics characterize the individuality of each semantic space. The combination of global metric and label-specific local metrics is utilized to construct latent semantic space for each label, in which similar intra-class instances are pushed closer and inter-class instances are pulled apart. Furthermore, metric-based label correlation regularization is constructed to maintain similarity between correlated label spaces. Extensive experiments on benchmark multi-label data sets validate the superiority of our proposed approach in learning effective distance metrics for multi-label classification.","doi":"10.24963/ijcai.2023/451","url":"https://ijcai.org/proceedings/2023/451","pdf":"https://ijcai.org/proceedings/2023/0451.pdf","markdown":"https://ijcai.org/proceedings/2023/451.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/451","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":451,"pages":{"first":4055,"last":4063},"keywords":["Machine Learning: ML: Classification","Machine Learning: ML: Multi-label"],"videos":[],"references":[{"position":1,"raw":"[Bellet et al., 2015] Aurélien Bellet, Amaury Habrard, and Marc Sebban. 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