Robust dual-graph regularized and minimum redundancy based on self-representation for semi-supervised feature selection

Abstract

Partial labeled data is ubiquitous in the big data era. Selecting informative features, and avoiding redundant and noise features is an important task for constructing robust learning models. The inherent characteristics of samples and features need to be surveyed simultaneously. In this study, a feature selection method for partial labeled data based on dual-graph regularized is proposed. Self-representation can well mine the relation between features. Then, the self-representation is combined with the framework of sparse learning to well embody the character of data and an adaptive redundancy regularization term based on self-representation is designed to minimize the redundancy between features. In the self-representation based regularization term, the relation between features is updated dynamically during the iteration so as to capture the inherent relation between features more accurately. The manifold structure in both feature space and data space are considered jointly. Moreover, the L2,p-norm is imposed on the feature self-representation regularization term, self-representation coefficient matrix, and feature selection matrix, respectively. These constrains aim to enhance its robustness to outliers, and select the representative features with discriminative and low redundancy. The convex (p=1) and non-convex (0<p<1) are involved in the proposed method. Then, a unified solution for the proposed method is investigated and its convergence is proved. Experimental results on public data sets show that the proposed method is effective for classification tasks when compared with some feature selection methods.