{"type":"ScholarlyArticle","title":"Binary Coding based Label Distribution Learning","authors":[{"name":"Ke Wang","slug":"ke-wang","given":"Ke","family":"Wang","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"},{"name":"Xin Geng","slug":"xin-geng","given":"Xin","family":"Geng","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"}],"abstract":"Label Distribution Learning (LDL) is a novel learning paradigm in machine learning, which assumes that an instance is labeled by a distribution over all labels, rather than labeled by a logic label or some logic labels. Thus, LDL can model the description degree of all possible labels to an instance. Although many LDL methods have been put forward to deal with different application tasks, most existing methods suffer from the scalability issue. In this paper, a scalable LDL framework named Binary Coding based Label Distribution Learning (BC-LDL) is proposed for large-scale LDL. The proposed framework includes two parts, i.e., binary coding and label distribution generation. In the binary coding part, the learning objective is to generate the optimal binary codes for the instances. We integrate the label distribution information of the instances into a binary coding procedure, leading to high-quality binary codes. In the label distribution generation part, given an instance, the k nearest training instances in the Hamming space are searched and the mean of the label distributions of all the neighboring instances is calculated as the predicted label distribution. Experiments on five benchmark datasets validate the superiority of BC-LDL over several state-of-the-art LDL methods.","doi":"10.24963/ijcai.2018/386","url":"https://ijcai.org/proceedings/2018/386","pdf":"https://ijcai.org/proceedings/2018/0386.pdf","markdown":"https://ijcai.org/proceedings/2018/386.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/386","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":386,"pages":{"first":2783,"last":2789},"keywords":["Machine Learning Applications: Big data ; Scalability","Machine Learning: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Gao et al., 2017] Bin-Bin Gao, Chao Xing, Chen-Wei Xie, Jianxin Wu, and Xin Geng. 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