关键词: 多标记学习, 谱特征选择, 标记关联性

Abstract: Feature selection has been deeply studied in traditional single label problem. When it comes to multi-label problem, most of traditional feature selection algorithms for single label problem are not able to be applied directly, since instances in multi-label problems are associated with several labels simultaneously, new criteria to evaluate features are needed. Because of the correlations among several labels, new methods to model labels structure are needed for using the correlation information when designing feature selection algorithm. This paper uses the spectral feature selection (SPEC) framework to handle multi-label problem and uses Jaccard similarity to construct the similarity matrix and the target graph in SPEC for multi-label problem. This method is under filter model, which is different from the wrapper model. The latter always transforms the multi-label problem into some single label problems, and traditional feature selection algorithm is applied to these single label problems. The experiments on real world data sets demonstrate the correctness and high performance of the proposed algorithm.

Key words: multi-label learning, spectral feature selection, label correlation