High-dimensional sign-constrained feature selection and grouping

Qin Shanshan; Ding Hao; Wu Yuehua; Liu Feng

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High-dimensional sign-constrained feature selection and grouping

机译：高维标记受限的特征选择和分组

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摘要

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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来源
《Annals of the Institute of Statistical Mathematics》 |2021年第4期|共33页
作者
Qin Shanshan; Ding Hao; Wu Yuehua; Liu Feng;
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作者单位

York Univ Dept Math &

Stat 4700 Keele St Toronto ON M3J 1P3 Canada;

York Univ Dept Math &

Stat 4700 Keele St Toronto ON M3J 1P3 Canada;

York Univ Dept Math &

Stat 4700 Keele St Toronto ON M3J 1P3 Canada;

Univ Technol Sydney Australian Artificial Intelligence Inst Sydney NSW 2007 Australia;

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原文格式 PDF
正文语种 eng
中图分类数理统计;
关键词
Difference convex programming; Feature grouping; Feature selection; High-dimensional; Non-negative;

机译：差异凸编程;特征分组;特征选择;高维;非负面;

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High-dimensional sign-constrained feature selection and grouping

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