Cluster feature selection in high-dimensional linear models

doi:10.1142/S2010326317500150

CSpace > 应用数学研究所

	Cluster feature selection in high-dimensional linear models
	Lin, Bingqing 1; Pang, Zhen 2; Wang, Qihua1,3
	2018
发表期刊	RANDOM MATRICES-THEORY AND APPLICATIONS
ISSN	2010-3263
卷号	7 期号:1 页码:23
摘要	This paper concerns with variable screening when highly correlated variables exist in high-dimensional linear models. We propose a novel cluster feature selection CFS) procedure based on the elastic net and linear correlation variable screening to enjoy the benefits of the two methods. When calculating the correlation between the predictor and the response, we consider highly correlated groups of predictors instead of the individual ones. This is in contrast to the usual linear correlation variable screening. Within each correlated group, we apply the elastic net to select variables and estimate their parameters. This avoids the drawback of mistakenly eliminating true relevant variables when they are highly correlated like LASSO [R. Tibshirani, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B 58 1996) 268-288] does. After applying the CFS procedure, the maximum absolute correlation coefficient between clusters becomes smaller and any common model selection methods like sure independence screening SIS) [J. Fan and J. Lv, Sure independence screening for ultrahigh dimensional feature space, J. R. Stat. Soc. Ser. B 70 2008) 849-911] or LASSO can be applied to improve the results. Extensive numerical examples including pure simulation examples and semi-real examples are conducted to show the good performances of our procedure.
关键词	Variable selection variable screening SIS elastic net
DOI	10.1142/S2010326317500150
语种	英语
资助项目	National Natural Science Foundation of China[11701386] ; National Natural Science Foundation of China[11626159] ; National Natural Science Foundation of China[11171331] ; National Natural Science Foundation of China[11331011] ; Hong Kong Polytechnic University (G-YBKQ) ; program for Creative Research Group of National Natural Science Foundation of China[61621003] ; Natural Science Foundation of SZU
WOS研究方向	Physics ; Mathematics
WOS类目	Physics, Mathematical ; Statistics & Probability
WOS记录号	WOS:000423849700003
出版者	WORLD SCI PUBL CO INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/29541
专题	应用数学研究所
通讯作者	Pang, Zhen
作者单位	1.Shenzhen Univ, Coll Math & Stat, Inst Stat Sci, Shenzhen 518060, Peoples R China 2.Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Lin, Bingqing,Pang, Zhen,Wang, Qihua. Cluster feature selection in high-dimensional linear models[J]. RANDOM MATRICES-THEORY AND APPLICATIONS,2018,7(1):23.
APA	Lin, Bingqing,Pang, Zhen,&Wang, Qihua.(2018).Cluster feature selection in high-dimensional linear models.RANDOM MATRICES-THEORY AND APPLICATIONS,7(1),23.
MLA	Lin, Bingqing,et al."Cluster feature selection in high-dimensional linear models".RANDOM MATRICES-THEORY AND APPLICATIONS 7.1(2018):23.