2014/08/26 by Xin Geng, Geng, Xin · 16 citations
Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Text and Document Classification Technologies
paper · pdf · doi:10.48550/arxiv.1408.6027
openalex publication_date 2014/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named label distribution learning (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which each label describes the instance. LDL is a more general learning framework which includes both single-label and multi-label learning as its special cases. This paper proposes six working LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design. In order to compare the performance of the LDL algorithms, six representative and diverse evaluation measures are selected via a clustering analysis, and the first batch of label distribution datasets are collected and made publicly available. Experimental results on one artificial and fifteen real-world datasets show clear advantages of the specialized algorithms, which indicates the importance of special design for the characteristics of the LDL problem.