{"type":"ScholarlyArticle","title":"Label distribution learning with label-specific features","authors":[{"name":"Tingting Ren","slug":"tingting-ren","given":"Tingting","family":"Ren","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology , China"},{"name":"Xiuyi Jia","slug":"xiuyi-jia","given":"Xiuyi","family":"Jia","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology , China; Jiangsu Key Laboratory of Big Data Security & Intelligent Processing , Nanjing University of Posts and Telecommunications , China; State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Weiwei Li","slug":"weiwei-li","given":"Weiwei","family":"Li","affiliation":"College of Astronautics , Nanjing University of Aeronautics and Astronautics , China"},{"name":"Lei Chen","slug":"lei-chen","given":"Lei","family":"Chen","affiliation":"Jiangsu Key Laboratory of Big Data Security & Intelligent Processing , Nanjing University of Posts and Telecommunications , China"},{"name":"Zechao Li","slug":"zechao-li","given":"Zechao","family":"Li","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology , China"}],"abstract":"Label distribution learning (LDL) is a novel machine learning paradigm to deal with label ambiguity issues by placing more emphasis on how relevant each label is to a particular instance. Many LDL algorithms have been proposed and most of them concentrate on the learning models, while few of them focus on the feature selection problem. All existing LDL models are built on a simple feature space in which all features are shared by all the class labels. However, this kind of traditional data representation strategy tends to select features that are distinguishable for all labels, but ignores label-specific features that are pertinent and discriminative for each class label. In this paper, we propose a novel LDL algorithm by leveraging label-specific features. The common features for all labels and specific features for each label are simultaneously learned to enhance the LDL model. Moreover, we also exploit the label correlations in the proposed LDL model. The experimental results on several real-world data sets validate the effectiveness of our method.","doi":"10.24963/ijcai.2019/460","url":"https://ijcai.org/proceedings/2019/460","pdf":"https://ijcai.org/proceedings/2019/0460.pdf","markdown":"https://ijcai.org/proceedings/2019/460.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/460","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":460,"pages":{"first":3318,"last":3324},"keywords":["Machine Learning: Feature Selection ; Learning Sparse Models","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Boyd et al., 2011] Stephen P. 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