{"type":"ScholarlyArticle","title":"Incomplete Label Distribution Learning","authors":[{"name":"Miao Xu","slug":"miao-xu","given":"Miao","family":"Xu","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University Nanjing 210023 , China"},{"name":"Zhi-Hua Zhou","slug":"zhi-hua-zhou","given":"Zhi-Hua","family":"Zhou","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University Nanjing 210023 , China"}],"abstract":"Label distribution learning (LDL) assumes labels can be associated to an instance to some degree, thus it can learn the relevance of a label to a particular instance. Although LDL has got successful practical applications, one problem with existing LDL methods is that they are designed for data with \\emph{complete} supervised information, while in reality, annotation information may be \\emph{incomplete}, because assigning each label a real value to indicate its association with a particular instance will result in large cost in labor and time. In this paper, we will solve LDL problem when given \\emph{incomplete} supervised information. We propose an objective based on trace norm minimization to exploit the correlation between labels. We develop a proximal gradient descend algorithm and an algorithm based on alternating direction method of multipliers. Experiments validate the effectiveness of our proposal.","doi":"10.24963/ijcai.2017/443","url":"https://ijcai.org/proceedings/2017/443","pdf":"https://ijcai.org/proceedings/2017/0443.pdf","markdown":"https://ijcai.org/proceedings/2017/443.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/443","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":443,"pages":{"first":3175,"last":3181},"keywords":["Machine Learning: Multi-instance/Multi-label/Multi-view learning","Machine Learning: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Bi and Kwok, 2014] Wei Bi and James T. 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