期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 37, 期 1, 页码 107-120出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2014.2339815
关键词
Machine learning; multi-label learning; label correlations; label-specific features
资金
- National Science Foundation of China [61175049, 61222309]
- MOE Program for New Century Excellent Talents in University [NCET-13-0130]
- Fundamental Research Funds for the Central Universities (the Cultivation Program for Young Faculties of Southeast University)
Multi-label learning deals with the problem where each example is represented by a single instance (feature vector) while associated with a set of class labels. Existing approaches learn from multi-label data by manipulating with identical feature set, i.e. the very instance representation of each example is employed in the discrimination processes of all class labels. However, this popular strategy might be suboptimal as each label is supposed to possess specific characteristics of its own. In this paper, another strategy to learn from multi-label data is studied, where label-specific features are exploited to benefit the discrimination of different class labels. Accordingly, an intuitive yet effective algorithm named LIFT, i.e. multi-label learning with Label specIfic FeaTures, is proposed. LIFT firstly constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results. Comprehensive experiments on a total of 17 benchmark data sets clearly validate the superiority of LIFT against other well-established multi-label learning algorithms as well as the effectiveness of label-specific features.
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