Efficient Sparse Group Feature Selection via Nonconvex Optimization

TL;DR

This paper introduces a nonconvex sparse group feature selection model that improves accuracy and consistency over traditional convex methods, supported by an efficient algorithm suitable for large-scale data.

Contribution

It presents a novel nonconvex model for sparse group feature selection that achieves oracle reconstruction and consistency, along with an efficient scalable algorithm.

Findings

Outperforms convex methods on synthetic data.

Achieves high accuracy in real-world applications.

Provides a scalable solution for large datasets.

Abstract

Sparse feature selection has been demonstrated to be effective in handling high-dimensional data. While promising, most of the existing works use convex methods, which may be suboptimal in terms of the accuracy of feature selection and parameter estimation. In this paper, we expand a nonconvex paradigm to sparse group feature selection, which is motivated by applications that require identifying the underlying group structure and performing feature selection simultaneously. The main contributions of this article are twofold: (1) statistically, we introduce a nonconvex sparse group feature selection model which can reconstruct the oracle estimator. Therefore, consistent feature selection and parameter estimation can be achieved; (2) computationally, we propose an efficient algorithm that is applicable to large-scale problems. Numerical results suggest that the proposed nonconvex method compares favorably against its competitors on synthetic data and real-world applications, thus achieving desired goal of delivering high performance.