Efficient sparse group feature selection via nonconvex optimization

Shuo Xiang, Xiaotong Shen, Jieping Ye

Research output: Contribution to conferencePaperpeer-review

35 Scopus citations


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.

Original languageEnglish (US)
Number of pages9
StatePublished - 2013
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: Jun 16 2013Jun 21 2013


Other30th International Conference on Machine Learning, ICML 2013
Country/TerritoryUnited States
CityAtlanta, GA


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