{"type":"ScholarlyArticle","title":"Discrete 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 , China 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 , China School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"}],"abstract":"Label Distribution Learning (LDL) is a general learning paradigm in machine learning, which includes both single-label learning (SLL) and multi-label learning (MLL) as its special cases. Recently, many LDL algorithms have been proposed to handle different application tasks such as facial age estimation, head pose estimation and visual sentiment distributions prediction. However, the training time complexity of most existing LDL algorithms is too high, which makes them unapplicable to large-scale LDL. In this paper, we propose a novel LDL method to address this issue, termed Discrete Binary Coding based Label Distribution Learning (DBC-LDL). Specifically, we design an efficiently discrete coding framework to learn binary codes for instances. Furthermore, both the pair-wise semantic similarities and the original label distributions are integrated into this framework to learn highly discriminative binary codes. In addition, a fast approximate nearest neighbor (ANN) search strategy is utilized to predict label distributions for testing instances. Experimental results on five real-world datasets demonstrate its superior performance over several state-of-the-art LDL methods with the lower time cost.","doi":"10.24963/ijcai.2019/518","url":"https://ijcai.org/proceedings/2019/518","pdf":"https://ijcai.org/proceedings/2019/0518.pdf","markdown":"https://ijcai.org/proceedings/2019/518.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/518","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":518,"pages":{"first":3733,"last":3739},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Machine Learning Applications: Big data ; Scalability","Machine Learning: Classification"],"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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