High-dimensional sign-constrained feature selection and grouping
- Publisher:
- Springer Science and Business Media LLC
- Publication Type:
- Journal Article
- Citation:
- Annals of the Institute of Statistical Mathematics, 2020, pp. 1-33
- Issue Date:
- 2020-01-01
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Filename | Description | Size | |||
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Qin2020_Article_High-dimensionalSign-constrain.pdf | Published version | 3.66 MB |
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© 2020, The Institute of Statistical Mathematics, Tokyo. 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.
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