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Cluster feature selection in high-dimensional linear models
Lin, Bingqing1; Pang, Zhen2; Wang, Qihua1,3
2018
Source PublicationRANDOM MATRICES-THEORY AND APPLICATIONS
ISSN2010-3263
Volume7Issue:1Pages:23
AbstractThis 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.
KeywordVariable selection variable screening SIS elastic net
DOI10.1142/S2010326317500150
Language英语
Funding ProjectNational 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 Research AreaPhysics ; Mathematics
WOS SubjectPhysics, Mathematical ; Statistics & Probability
WOS IDWOS:000423849700003
PublisherWORLD SCI PUBL CO INC
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/29541
Collection应用数学研究所
Affiliation1.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
Recommended Citation
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.
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