An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression

doi:10.1038/s41598-021-03323-7

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	An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression
	Yu, Xue ; Sun, Yifan ; Zhou, Hai-Jun
刊名	SCIENTIFIC REPORTS
	2021
卷号	11 期号:1 页码:24034
关键词	VARIABLE SELECTION ALGORITHMS SHRINKAGE INFERENCE MODELS LASSO
ISSN号	2045-2322
DOI	10.1038/s41598-021-03323-7
英文摘要	High-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression coefficients. In this paper, we propose a simple heuristic algorithm to construct sparse high-dimensional linear regression models, which is adapted from the shortest-solution guided decimation algorithm and is referred to as ASSD. This algorithm constructs the support of regression coefficients under the guidance of the shortest least-squares solution of the recursively decimated linear models, and it applies an early-stopping criterion and a second-stage thresholding procedure to refine this support. Our extensive numerical results demonstrate that ASSD outperforms LASSO, adaptive LASSO, vector approximate message passing, and two other representative greedy algorithms in solution accuracy and robustness. ASSD is especially suitable for linear regression problems with highly correlated measurement matrices encountered in real-world applications.
学科主题	Science & Technology - Other Topics
语种	英语
内容类型	期刊论文
源URL	[http://ir.itp.ac.cn/handle/311006/27588]
专题	理论物理研究所_理论物理所1978-2010年知识产出
作者单位	1.Renmin Univ China, Ctr Appl Stat, Sch Stat, Beijing 100872, Peoples R China 2.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China 4.MinJiang Univ, MinJiang Collaborat Ctr Theoret Phys, Fuzhou 350108, Peoples R China
推荐引用方式 GB/T 7714	Yu, Xue,Sun, Yifan,Zhou, Hai-Jun. An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression[J]. SCIENTIFIC REPORTS,2021,11(1):24034.
APA	Yu, Xue,Sun, Yifan,&Zhou, Hai-Jun.(2021).An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression.SCIENTIFIC REPORTS,11(1),24034.
MLA	Yu, Xue,et al."An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression".SCIENTIFIC REPORTS 11.1(2021):24034.