{"type":"ScholarlyArticle","title":"Privileged label enhancement with multi-label learning","authors":[{"name":"Wenfang Zhu","slug":"wenfang-zhu","given":"Wenfang","family":"Zhu","affiliation":"Key Laboratory of Information Perception and Systems for Public Security of MIIT , Nanjing University of Science and Technology , China; Jiangsu Key Lab of Image and Video Understanding for Social Security , Nanjing University of Science and Technology , China"},{"name":"Xiuyi Jia","slug":"xiuyi-jia","given":"Xiuyi","family":"Jia","affiliation":"Key Laboratory of Information Perception and Systems for Public Security of MIIT , Nanjing University of Science and Technology , China; Jiangsu Key Lab of Image and Video Understanding for Social Security , Nanjing University of Science and Technology , 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"}],"abstract":"Label distribution learning has attracted more and more attention in view of its more generalized ability to express the label ambiguity. However, it is much more expensive to obtain the label distribution information of the data rather than the logical labels. Thus, label enhancement is proposed to recover the label distributions from the logical labels. In this paper, we propose a novel label enhancement method by using privileged information. We first apply a multi-label learning model to implicitly capture the complex structural information between instances and generate the privileged information. Second, we adopt LUPI (learning with privileged information) paradigm to utilize the privileged information and employ RSVM+ as\r\nthe prediction model. Finally, comparison experiments on 12 datasets demonstrate that our proposal can better fit the ground-truth label distributions.","doi":"10.24963/ijcai.2020/329","url":"https://ijcai.org/proceedings/2020/329","pdf":"https://ijcai.org/proceedings/2020/0329.pdf","markdown":"https://ijcai.org/proceedings/2020/329.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/329","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":329,"pages":{"first":2376,"last":2382},"keywords":["Machine Learning: Structured Prediction","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24650/file_24650.m3u8","length_sec":296,"provider":"hls"}],"references":[{"position":1,"raw":"[Boyd et al., 2011] Stephen P. 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