Dual-graph regularized subspace learning based feature selection

Abstract

Feature selection has attracted widespread attention with the massive growth of high-dimensional data. In recent years, all kinds of unsupervised feature selection methods have been presented. However, most of these methods can not fully explore the local geometric structure of the original data, which has been proven very important in unsupervised feature selection. To tackle this problem, we present a novel feature selection algorithm called dual-graph subspace learning based feature selection (DGSLFS). Specifically, on one hand, DGSLFS conducts feature selection procedures based on subspace learning, which can guarantee the useful information hidden in the original space be well exploited. On the other hand, we develop two novel graphs on samples and features, respectively, which can well preserve the local geometric structures. In addition, we impose an ℓ2,1-norm to constrain the reconstruction error term and the feature selection matrix. Thus, DGSLFS is robust to outliers and noises, and can guarantee the sparsity of features. The experimental results on several popular datasets show that our proposed algorithm can obtain encouraging results in comparison with some state-of-the-art algorithms.