Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model
文献类型：期刊论文
| 作者 | Hao, Meiling5; Qu, Lianqiang4; Kong, Dehan3; Sun, Liuquan2 ; Zhu, Hongtu1
|
| 刊名 | JOURNAL OF MACHINE LEARNING RESEARCH
 |
| 出版日期 | 2021 |
| 卷号 | 22页码:39 |
| 关键词 | High dimension Imaging genetics Matrix linear regression Optimal mini-max rate Variable selection |
| ISSN号 | 1532-4435 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000687116300001 |
| 出版者 | MICROTOME PUBL |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/59119]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Sun, Liuquan |
| 作者单位 | 1.Univ N Carolina, Dept Biostat, Chapel Hill, NC 27599 USA 2.Chinese Acad Sci, Inst Appl Math, Acad Math & Syst Sci, Beijing 100190, Peoples R China 3.Univ Toronto, Dept Stat Sci Univ, Toronto, ON M5G 1X6, Canada 4.Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China 5.Univ Int Business & Econ, Sch Stat, Beijing 100029, Peoples R China |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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