{"type":"ScholarlyArticle","title":"Discriminative and Correlative Partial Multi-Label Learning","authors":[{"name":"Haobo Wang","slug":"haobo-wang","given":"Haobo","family":"Wang","affiliation":"Key Lab of Intelligent Computing Based Big Data of Zhejiang Province , Zhejiang University; College of Computer Science and Technology , Zhejiang University"},{"name":"Weiwei Liu","slug":"weiwei-liu","given":"Weiwei","family":"Liu","affiliation":"School of Computer Science , Wuhan University"},{"name":"Yang Zhao","slug":"yang-zhao","given":"Yang","family":"Zhao","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Chen Zhang","slug":"chen-zhang","given":"Chen","family":"Zhang","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Tianlei Hu","slug":"tianlei-hu","given":"Tianlei","family":"Hu","affiliation":"Key Lab of Intelligent Computing Based Big Data of Zhejiang Province , Zhejiang University; College of Computer Science and Technology , Zhejiang University"},{"name":"Gang Chen","slug":"gang-chen","given":"Gang","family":"Chen","affiliation":"Key Lab of Intelligent Computing Based Big Data of Zhejiang Province , Zhejiang University; College of Computer Science and Technology , Zhejiang University"}],"abstract":"In partial label learning (PML), each instance is associated with a candidate label set that contains multiple relevant labels and other false positive labels. The most challenging issue for the PML is that the training procedure is prone to be affected by the labeling noise. We observe that state-of-the-art PML methods are either powerless to disambiguate the correct labels from the candidate labels or incapable of extracting the label correlations sufficiently. To fill this gap, a two-stage DiscRiminative and correlAtive partial Multi-label leArning (DRAMA) algorithm is presented in this work. In the first stage, a confidence value is learned for each label by utilizing the feature manifold, which indicates how likely a label is correct. In the second stage, a gradient boosting model is induced to fit the label confidences. Specifically, to explore the label correlations, we augment the feature space by the previously elicited labels on each boosting round. Extensive experiments on various real-world datasets clearly validate the superiority of our proposed method.","doi":"10.24963/ijcai.2019/512","url":"https://ijcai.org/proceedings/2019/512","pdf":"https://ijcai.org/proceedings/2019/0512.pdf","markdown":"https://ijcai.org/proceedings/2019/512.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/512","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":512,"pages":{"first":3691,"last":3697},"keywords":["Machine Learning: Dimensionality Reduction and Manifold Learning","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Boutell et al., 2004] Matthew R. 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