{"type":"ScholarlyArticle","title":"Exploiting Multi-Label Correlation in Label Distribution Learning","authors":[{"name":"Zhiqiang Kou","slug":"zhiqiang-kou","given":"Zhiqiang","family":"Kou","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Jing Wang","slug":"jing-wang","given":"Jing","family":"Wang","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Jiawei Tang","slug":"jiawei-tang","given":"Jiawei","family":"Tang","affiliation":"Chien-Shiung WU College , Southeast University , Nanjing 210096 , China"},{"name":"Yuheng Jia","slug":"yuheng-jia","given":"Yuheng","family":"Jia","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Boyu Shi","slug":"boyu-shi","given":"Boyu","family":"Shi","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Xin Geng","slug":"xin-geng","given":"Xin","family":"Geng","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"}],"abstract":"Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Numerous  LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space; among these, many exploited the low-rank structure of label distribution to capture label correlation. However, recent research has unveiled that label distribution matrices typically maintain full rank, posing a challenge to approaches relying on low-rank label correlation. Notably, low-rank label correlation finds widespread adoption in multi-label learning (MLL) literature due to the often low-rank nature of multi-label matrices. Inspired by that, we introduce an auxiliary MLL process within the LDL framework, focusing on capturing low-rank label correlation within this auxiliary MLL component rather than the LDL itself. By doing so, we adeptly exploited low-rank label correlation in our LDL methods. We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL.","doi":"10.24963/ijcai.2024/478","url":"https://ijcai.org/proceedings/2024/478","pdf":"https://ijcai.org/proceedings/2024/0478.pdf","markdown":"https://ijcai.org/proceedings/2024/478.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/478","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":478,"pages":{"first":4326,"last":4334},"keywords":["Machine Learning: ML: Multi-label learning","Machine Learning: ML: Classification","Machine Learning: ML: Applications"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2010] Jian-Feng Cai, Emmanuel J Candès, and Zuowei Shen. 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