Simultaneous variable selection and class fusion with penalized distance criterion based classifiers

doi:10.1016/j.csda.2018.09.002

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	Simultaneous variable selection and class fusion with penalized distance criterion based classifiers
	Sheng, Ying 1; Wang, Qihua1,2
	2019-05-01
发表期刊	COMPUTATIONAL STATISTICS & DATA ANALYSIS
ISSN	0167-9473
卷号	133 页码:138-152
摘要	Two new methods are proposed to solve the problem of constructing multiclass classifiers, selecting important variables for classification and determining corresponding discriminative variables for each pair of classes simultaneously in the high-dimensional setting. Different from existing methods, which are based on the separate estimation of the precision matrix and mean vectors, the proposed methods construct classifiers by estimating products of the precision matrix and mean vectors or all discriminant directions directly with appropriate penalties. This leads to the use of the distance criterion instead of the log-likelihood used in the existing literature. The proposed methods can not only consistently select important variables for classification but also consistently determine corresponding discriminative variables for each pair of classes. For the multiclass classification problem, conditional misclassification error rates of classifiers constructed by the proposed methods converge to the misclassification error rate of the Bayes rule in probability and rates of convergence are also obtained. Finally, simulations and the real data analysis well demonstrate good performances of the proposed methods in comparison with existing methods. (C) 2018 Elsevier B.V. All rights reserved.
关键词	Linear discriminant analysis Discriminant directions Variable selection Class fusion Misclassification error rate
DOI	10.1016/j.csda.2018.09.002
语种	英语
资助项目	National Natural Science Foundation of China[11871460] ; National Natural Science Foundation of China[11331011] ; program for Creative Research Group in China[61621003] ; Key Lab of Random Complex Structure and Data Science, CAS, China
WOS研究方向	Computer Science ; Mathematics
WOS类目	Computer Science, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:000460719200010
出版者	ELSEVIER SCIENCE BV
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/33337
专题	应用数学研究所
通讯作者	Wang, Qihua
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Zhejiang Gongshang Univ, Sch Stat & Math, Hangzhou 310018, Zhejiang, Peoples R China
推荐引用方式 GB/T 7714	Sheng, Ying,Wang, Qihua. Simultaneous variable selection and class fusion with penalized distance criterion based classifiers[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS,2019,133:138-152.
APA	Sheng, Ying,&Wang, Qihua.(2019).Simultaneous variable selection and class fusion with penalized distance criterion based classifiers.COMPUTATIONAL STATISTICS & DATA ANALYSIS,133,138-152.
MLA	Sheng, Ying,et al."Simultaneous variable selection and class fusion with penalized distance criterion based classifiers".COMPUTATIONAL STATISTICS & DATA ANALYSIS 133(2019):138-152.