Label correlations variation for robust multi-label feature selection

Abstract

Numerous high-dimension multi-label data are produced, leading to the imperative need to design excellent multi-label feature selection methods. It is of paramount importance to exploit label correlations in previous methods. However, there exist two unsolved issues in most of existing methods. First, most of existing methods explore label correlations based on the original label space with redundant and irrelevant label information. Second, previous methods either consider second-order label correlations or high-order label correlations, in fact, both two types of label correlations are significant and complementary for capturing label information. To this end, this paper proposes a robust multi-label feature selection method with both two types of label correlations. To start with, we introduce the self-expression model to consider the high-order label correlations, additionally, the l2,1-norm is imposed onto the self-expression coefficient matrix to eliminate redundant and noisy information. Furthermore, we employ a label-level regularizer to achieve pairwise label correlations. Finally, an optimization scheme with convergence proof is designed to deal with the objective function. Multiple experimental analysis results on fourteen multi-label data sets manifest that the classification performance of the proposed method outperforms other baselines.