Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model

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	Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model
	Hao, Meiling 1; Qu, Lianqiang 2; Kong, Dehan 3; Sun, Liuquan4 ; Zhu, Hongtu 5
	2021
发表期刊	JOURNAL OF MACHINE LEARNING RESEARCH
ISSN	1532-4435
卷号	22 页码:39
摘要	Large-scale matrix linear regression models with high-dimensional responses and high dimensional variables have been widely employed in various large-scale biomedical studies. In this article, we propose an optimal minimax variable selection approach for the matrix linear regression model when the dimensions of both the response matrix and predictors diverge at the exponential rate of the sample size. We develop an iterative hard-thresholding algorithm for fast computation and establish an optimal minimax theory for the parameter estimates. The finite sample performance of the method is examined via extensive simulation studies and a real data application from the Alzheimer's Disease Neuroimaging Initiative study is provided.
关键词	High dimension Imaging genetics Matrix linear regression Optimal mini-max rate Variable selection
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[11901087] ; National Natural Science Foundation of China[12001219] ; National Natural Science Foundation of China[11771431] ; National Natural Science Foundation of China[11690015] ; Program for Young Excellent Talents, UIBE[19YQ15] ; Natural Science and Engineering Research Council of Canada[RGPIN-2017-06538] ; Natural Science and Engineering Research Council of Canada[RGPAS-2017-507944] ; Hubei Natural Science Foundation of China[2018CFB256] ; Fundamental Research Funds for the Central Universities in CCNU ; Key Laboratory of Random Structures and Data Science, Chinese Academy of Sciences[2008DP173182]
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence
WOS记录号	WOS:000687116300001
出版者	MICROTOME PUBL
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59120
专题	应用数学研究所
通讯作者	Sun, Liuquan
作者单位	1.Univ Int Business & Econ, Sch Stat, Beijing 100029, Peoples R China 2.Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China 3.Univ Toronto, Dept Stat Sci Univ, Toronto, ON M5G 1X6, Canada 4.Chinese Acad Sci, Inst Appl Math, Acad Math & Syst Sci, Beijing 100190, Peoples R China 5.Univ N Carolina, Dept Biostat, Chapel Hill, NC 27599 USA
推荐引用方式 GB/T 7714	Hao, Meiling,Qu, Lianqiang,Kong, Dehan,et al. Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model[J]. JOURNAL OF MACHINE LEARNING RESEARCH,2021,22:39.
APA	Hao, Meiling,Qu, Lianqiang,Kong, Dehan,Sun, Liuquan,&Zhu, Hongtu.(2021).Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model.JOURNAL OF MACHINE LEARNING RESEARCH,22,39.
MLA	Hao, Meiling,et al."Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model".JOURNAL OF MACHINE LEARNING RESEARCH 22(2021):39.