Feature selection for label distribution learning via feature similarity and label correlation

Feature selection plays a crucial role in machine learning and data mining, and improves the performance of learning models by selecting a distinguishing feature subset and eliminating irrelevant features. Existing feature selection methods are mainly used for single-label learning and multi-label learning; however, there are only a few feature selection methods for label distribution learning. Label distribution learning has the “curse of dimensionality” problem, similar to that in multi-label learning. In label distribution learning, the related labels of each sample have different levels of importance. Therefore, multi-label feature selection algorithms can not be directly applied to label distribution data, and discretizing the label distribution data into multi-label data would result in the loss of some important supervised information. To solve this problem, a novel feature selection algorithm for label distribution learning is proposed in this paper. The proposed method utilizes neighborhood granularity to explore feature similarity, and it uses a correlation coefficient to generate the label correlations. In addition, sparse learning is used to improve the robustness and control complexity. Experimental results indicate that our proposed method is more effective than five state-of-art feature selection algorithms on twelve datasets, with respect to six representative evaluation measures.