Unsupervised feature selection based on bipartite graph and low-redundant regularization

Abstract

Unsupervised feature selection (UFS) has attracted increasing attention because of the difficulty and high cost of obtaining data labels. Since the ignorance of redundancy between features and the use of a fixed similarity matrix, current UFS algorithms do not perform well. Moreover, because of the high computational complexity, existing UFS algorithms usually cannot handle large-scale data. A novel UFS method is proposed to solve these problems through bipartite graph learning with low-redundant regularization (BGLR). Firstly, BGLR uses a variance-based de-correlation anchor selection method to ensure that selected anchors can be evenly distributed across classes. Secondly, BGLR builds an adaptive bipartite graph by using selected anchors and original data in the projection space. L2,0-norm constraint is applied to the projection matrix. Thus, the feature subset can be obtained directly. Finally, a regularization term is applied to control the redundancy of selected features. Moreover, BGLR is solved by an iterative method, which can converge quickly. Experiments are performed on public datasets in comparison with some relevant methods. The experimental results demonstrate the BGLR’s effectiveness.