Weighted structure preservation and redundancy minimization for feature selection

The recent literature indicates that structure preserving is of great importance for feature selection and many existing selection criteria essentially work in this way. In this paper, we argue that the Eigen value decomposition of global pair wise similarity matrix should be weighted, and the redundancy among the features should be minimized. In order to show this, we propose a weighted structure preservation and features redundancy minimization framework for feature selection. In this framework, the Eigen vector obtained by the Eigen decomposition of global pair wise similarity matrix is weighted by the corresponding Eigen value, and the cosine distance between two features together with the L2,1 norm of these two features are used to evaluate the degree of redundancy between these two features. A comprehensive experimental study is then conducted in order to compare our feature selection algorithms with many state-of-the art ones in supervised learning scenarios. The conducted experiments validate the effectiveness of our feature selection.