Dual-dual subspace learning with low-rank consideration for feature selection

Abstract

The performance of machine learning algorithms can be affected by redundant features of high-dimensional data. Furthermore, these irrelevant features increase the time of computation for learning model. These problems can be addressed by leveraging different techniques including feature selection and dimensionality reduction. Unsupervised feature selection has drawn increased attention due to the difficulty of label collection for supervised feature selection. To this end, we developed an innovative approach based on nonnegative matrix factorization (NMF) to remove redundant information. In this technique, for the first time, the local information preserving regularization and global information preserving regularization are applied for both feature weight matrix and representation matrix which is why we called Dual-Dual regularized feature selection. Furthermore, Schatten p-norm is utilized to extract inherent low-rank properties of data. To demonstrate the effectiveness of the proposed method, experimental studies are conducted on six benchmark datasets. The computational results show that the proposed method in comparison with state-of-the-art unsupervised feature selection techniques is more efficient for feature selection.