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Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model
Hao, Meiling1; Qu, Lianqiang2; Kong, Dehan3; Sun, Liuquan4; Zhu, Hongtu5
2021
Source PublicationJOURNAL OF MACHINE LEARNING RESEARCH
ISSN1532-4435
Volume22Pages:39
AbstractLarge-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.
KeywordHigh dimension Imaging genetics Matrix linear regression Optimal mini-max rate Variable selection
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence
WOS IDWOS:000687116300001
PublisherMICROTOME PUBL
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/59120
Collection应用数学研究所
Corresponding AuthorSun, Liuquan
Affiliation1.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
Recommended Citation
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.
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