Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation

Abstract

Recent years, joint feature selection and multi-label learning have received extensive attention as an open problem. However, there exist three general issues in previous multi-label feature selection methods. First of all, existing methods either consider local label correlations or global label correlations when they design multi-label feature selection methods, in fact, both two types of label correlations are significant for feature selection; second, previous methods use the low-quality graph to excavate local label correlations so that the results of these methods are under-performing; third, feature redundancy is ignored by most of the sparse learning methods. To overcome these challenges, we preserve global label correlations and dynamic local label correlations by preserving graph structure. Additionally, the l2,1-norm and an inner product regularization term are imposed onto the objective function to preserve robust high row-sparsity and to select low redundant features. All the above terms are integrated into one learning framework, and then we utilize a simple yet effective scheme to optimize the framework. Experimental results demonstrate the classification superiority of the proposed method.