Sparse regularization based feature selection: A survey

Abstract

Feature selection, as an essential preprocessing tool, aims to identify a subset of crucial features by eliminating redundant and noisy features according to a predefined criterion. In recent years, sparse learning has received considerable attention. As an important component of sparse learning, sparse regularization has been widely used in feature selection to enforce irrelevant feature coefficients to be small or exactly zero, which is especially suitable for selecting the discriminative and relevant features. In this paper, we attempt to provide a survey on various sparse regularization based methods. We group the existing sparse regularization based methods into two categories, i.e., convex sparse regularization based feature selection and non-convex sparse regularization based feature selection. This survey not only compares the common and different characters of sparse regularizations based feature selection methods, but also offers useful guidance for practitioners of feature selection.