KMS Of Academy of mathematics and systems sciences, CAS
Simultaneous variable selection and class fusion with penalized distance criterion based classifiers | |
Sheng, Ying1; 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. |
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