{"type":"ScholarlyArticle","title":"Partial Label Learning with Unlabeled Data","authors":[{"name":"Qian-Wei Wang","slug":"qian-wei-wang","given":"Qian-Wei","family":"Wang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University"},{"name":"Yu-Feng Li","slug":"yu-feng-li","given":"Yu-Feng","family":"Li","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University"},{"name":"Zhi-Hua Zhou","slug":"zhi-hua-zhou","given":"Zhi-Hua","family":"Zhou","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University"}],"abstract":"Partial label learning deals with training examples each associated with a set of candidate labels, among which only one label is valid. Previous studies typically assume that the candidate label sets are provided for all training examples. In many real-world applications such as video character classification, however, it is generally difficult to label a large number of instances and there exists much data left to be unlabeled. We call this kind of problem semi-supervised partial label learning. In this paper, we propose the SSPL method to address this problem. Specifically, an iterative label propagation procedure between partial label examples and unlabeled instances is employed to disambiguate the candidate label sets of partial label examples as well as assign valid labels to unlabeled instances. The importance of unlabeled instances increases adaptively as the number of iteration increases, since they carry richer labeling information. Finally, unseen instances are classified based on the minimum reconstruction error on both partial label and unlabeled instances. Experiments on real-world data sets clearly validate the effectiveness of the proposed SSPL method.","doi":"10.24963/ijcai.2019/521","url":"https://ijcai.org/proceedings/2019/521","pdf":"https://ijcai.org/proceedings/2019/0521.pdf","markdown":"https://ijcai.org/proceedings/2019/521.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/521","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 M-Z","track":"Main track","number":521,"pages":{"first":3755,"last":3761},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Classification","Machine Learning: Semi-Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Blum and Chawla, 2001] A. 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