{"type":"ScholarlyArticle","title":"Latent Semantics Encoding for Label Distribution Learning","authors":[{"name":"Suping Xu","slug":"suping-xu","given":"Suping","family":"Xu","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China Department of Computer Science and Technology ,"},{"name":"Lin Shang","slug":"lin-shang","given":"Lin","family":"Shang","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China Department of Computer Science and Technology ,"},{"name":"Furao Shen","slug":"furao-shen","given":"Furao","family":"Shen","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China Department of Computer Science and Technology ,; Nanjing University , Nanjing 210023 , China"}],"abstract":"Label distribution learning (LDL) is a newly arisen learning paradigm to deal with label ambiguity problems, which can explore the relative importance of different labels in the description of a particular instance. Although some existing LDL algorithms have achieved better effectiveness in real applications, most of them typically emphasize on improving the learning ability by manipulating the label space, while ignoring the fact that irrelevant and redundant features exist in most practical classification learning tasks, which increase not only storage requirements but also computational overheads. Furthermore, noises in data acquisition will bring negative effects on the generalization performance of LDL algorithms. In this paper, we propose a novel algorithm, i.e., Latent Semantics Encoding for Label Distribution Learning (LSE-LDL), which learns the label distribution and implements feature selection simultaneously under the guidance of latent semantics. Specifically, to alleviate noise disturbances, we seek and encode discriminative original physical/chemical features into advanced latent semantic features, and then construct a mapping from the encoded semantic space to the label space via empirical risk minimization. Empirical studies on 15 real-world data sets validate the effectiveness of the proposed algorithm.","doi":"10.24963/ijcai.2019/553","url":"https://ijcai.org/proceedings/2019/553","pdf":"https://ijcai.org/proceedings/2019/0553.pdf","markdown":"https://ijcai.org/proceedings/2019/553.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/553","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":553,"pages":{"first":3982,"last":3988},"keywords":["Machine Learning: Data Mining","Machine Learning: Feature Selection ; Learning Sparse Models","Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Berger et al., 1996] Adam L. 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