Efficient Sparse Group Feature Selection via Nonconvex Optimization
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(1):284-292, 2013.
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) computationally, we introduce a nonconvex sparse group feature selection model and present an efficient optimization algorithm, of which the key step is a projection with two coupled constraints; (2) statistically, we show that the proposed model can reconstruct the oracle estimator. Therefore, consistent feature selection and parameter estimation can be achieved. Numerical results on synthetic and real-world data suggest that the proposed nonconvex method compares favorably against its competitors, thus achieving desired goal of delivering high performance.