{"type":"ScholarlyArticle","title":"Learning Label-Specific Multiple Local Metrics for Multi-Label Classification","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":"Jun-Yi Hang","slug":"jun-yi-hang","given":"Jun-Yi","family":"Hang","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":"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-label metric learning serve as an effective strategy to facilitate multi-label classification, aiming to learn better similarity metrics from multi-label examples. Existing multi-label metric learning approaches learn consistent metrics across all multi-label instances in the label space. However, such consistent metric learning approaches are suboptimal as they neglect the nonlinear distribution characteristics of multi-label instances. In this paper, we present LSMM, a label-specific multi-metric learning framework for multi-label classification, where nonlinear distribution characteristics of multi-label examples are considered by learning label-specific multiple local metrics for different instances on the shoulder of a global one. Specifically, multi-label instances within each label space can be divided into several disjoint partitions through either semantic-based or cluster-based partition strategies, in each of which a local metric is trained to separate the instances locally. Besides, a global metric is introduced to implicitly exploit high-order label correlations across all labels. The combination of the global metric and label-specific local metrics is utilized to measure the semantic similarities between multi-label instances in each label space, under which similar intra-class instances are pushed closer and inter-class instances are pulled apart. Comprehensive experiments on benchmark multi-label data sets validate the superiority of LSMM in learning effective similarity metrics for multi-label classification.","doi":"10.24963/ijcai.2024/524","url":"https://ijcai.org/proceedings/2024/524","pdf":"https://ijcai.org/proceedings/2024/0524.pdf","markdown":"https://ijcai.org/proceedings/2024/524.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/524","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":524,"pages":{"first":4742,"last":4750},"keywords":["Machine Learning: ML: Classification","Machine Learning: ML: Multi-label learning"],"videos":[],"references":[{"position":1,"raw":"[Bellet et al., 2015] Aurélien Bellet, Amaury Habrard, and Marc Sebban. 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