Multi-label feature selection via latent representation learning and dynamic graph constraints

Abstract

As an effective method to deal with the curse of dimensionality, multi-label feature selection aims to select the most representative subset of features by eliminating unfavorable features. Although great progress has been made in this field, how to mine adequate supervisory information from multi-label data remains a key challenge. Compared to the latent information of instances, the latent information of instance relevance contains both the basic information of instances and the latent relevance between instances. Base on this knowledge, we propose a novel multi-label feature selection method named LRDG that explores latent representation learning and dynamic graph constraints. Specifically, we introduce the latent representation of instance relevance as supervisory information for pseudo-label learning, and minimize information loss during pseudo-label learning by means of the label manifold, the non-negative constraints, and the minimization of the Frobenius norm between pseudo-labels and ground-truth labels. In addition, considering the shortcomings brought by traditional graph regularization, we propose to use the dynamic graph constructed from low-dimensional pseudo-labels to constrain feature weights. Extensive experiments on various multi-label datasets demonstrate the effectiveness of the proposed method. The code is available at https://github.com/yunbao520/LRDG.