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High-dimensional sign-constrained feature selection and grouping

Author

Listed:
  • Shanshan Qin

    (York University)

  • Hao Ding

    (York University)

  • Yuehua Wu

    (York University)

  • Feng Liu

    (University of Technology Sydney)

Abstract

In this paper, we propose a non-negative feature selection/feature grouping (nnFSG) method for general sign-constrained high-dimensional regression problems that allows regression coefficients to be disjointly homogeneous, with sparsity as a special case. To solve the resulting non-convex optimization problem, we provide an algorithm that incorporates the difference of convex programming, augmented Lagrange and coordinate descent methods. Furthermore, we show that the aforementioned nnFSG method recovers the oracle estimate consistently, and that the mean-squared errors are bounded. Additionally, we examine the performance of our method using finite sample simulations and applying it to a real protein mass spectrum dataset.

Suggested Citation

  • Shanshan Qin & Hao Ding & Yuehua Wu & Feng Liu, 2021. "High-dimensional sign-constrained feature selection and grouping," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 787-819, August.
    • Handle: RePEc:spr:aistmt:v:73:y:2021:i:4:d:10.1007_s10463-020-00766-z
    DOI: 10.1007/s10463-020-00766-z
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    References listed on IDEAS

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