{"type":"ScholarlyArticle","title":"Towards Robust Multi-Label Learning against Dirty Label Noise","authors":[{"name":"Yuhai Zhao","slug":"yuhai-zhao","given":"Yuhai","family":"Zhao","affiliation":"School of Computer Science and Engineering , Northeastern University , China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education , Northeastern University , China"},{"name":"Yejiang Wang","slug":"yejiang-wang","given":"Yejiang","family":"Wang","affiliation":"School of Computer Science and Engineering , Northeastern University , China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education , Northeastern University , China"},{"name":"Zhengkui Wang","slug":"zhengkui-wang","given":"Zhengkui","family":"Wang","affiliation":"InfoComm Technology Cluster , Singapore Institute of Technology , Singapore"},{"name":"Wen Shan","slug":"wen-shan","given":"Wen","family":"Shan","affiliation":"Singapore University of Social Sciences , Singapore"},{"name":"Miaomiao Huang","slug":"miaomiao-huang","given":"Miaomiao","family":"Huang","affiliation":"School of Computer Science and Engineering , Northeastern University , China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education , Northeastern University , China"},{"name":"Meixia Wang","slug":"meixia-wang","given":"Meixia","family":"Wang","affiliation":"School of Computer Science and Engineering , Northeastern University , China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education , Northeastern University , China"},{"name":"Min Huang","slug":"min-huang","given":"Min","family":"Huang","affiliation":"College of Information Science and Engineering , Northeastern University , China"},{"name":"Xingwei Wang","slug":"xingwei-wang","given":"Xingwei","family":"Wang","affiliation":"School of Computer Science and Engineering , Northeastern University , China"}],"abstract":"In multi-label learning, one of the major challenges is that the data are associated with label noise including the random noisy labels (e.g., data encoding errors) and noisy labels created by annotators (e.g., missing, extra, or error label), where noise is promoted by different structures (e.g., gaussian, sparse or subjective). Existing methods are tailored to handle noise with one specific structure. However, they lack of consideration of the fact that the data are always with dirty noisy labels, simutaneously gaussian, sparse and subjective, in real applications. In this paper, we formalize the multi-label learning with dirty noise as a new learning problem, namely Noisy Multi-label Learning (NML). To solve the NML problem, we decompose a corrupted label matrix as the noise matrix plus a true label matrix (maybe high-rank). For the noise matrix, a mixed norm penalty is developed as regularizer for dirty noise distribution. Under this norm, the conditions required for exact noise recovery are provided theoretically. For the true label matrix that is not necessarily low-rank, we apply a non-linear mapping to ensure its low-rankness such that the high-order label correlation can be utilized. Experimental results show that the proposed method outperforms the state-of-the-art methods significantly.","doi":"10.24963/ijcai.2024/617","url":"https://ijcai.org/proceedings/2024/617","pdf":"https://ijcai.org/proceedings/2024/0617.pdf","markdown":"https://ijcai.org/proceedings/2024/617.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/617","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":617,"pages":{"first":5581,"last":5589},"keywords":["Machine Learning: ML: Optimization","Machine Learning: ML: Weakly supervised learning","Machine Learning: ML: Multi-label learning"],"videos":[],"references":[{"position":1,"raw":"[Bauschke and Combettes, 2017] Heinz H Bauschke and Patrick L Combettes. 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