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	L1 least squares for sparse high-dimensional LDA
	Li, Yanfang ; Jia, Jinzhu
	2017
关键词	High-dimensional LDA Lasso sparsity LINEAR DISCRIMINANT-ANALYSIS LASSO CLASSIFICATION REGRESSION
英文摘要	This paper studies high-dimensional linear discriminant analysis (LDA). First, we review the l(1) penalized least square LDA proposed in [10], which could circumvent estimation of the annoying high-dimensional covariance matrix. Then detailed theoretical analyses of this sparse LDA are established. To be specific, we prove that the penalized estimator is l(2) consistent in high-dimensional regime and the misclassification error rate of the penalized LDA is asymptotically optimal under a set of reasonably standard regularity conditions. The theoretical results are complementary to the results to [10], together with which we have more understanding of the l(1) penalized least square LDA (or called Lassoed LDA).; National Science Foundation of China [11101005, 11571021]; Key Lab of Mathematical Economics and Quantitative Finance (Ministry of Education); Key lab of Mathematics and Applied Mathematics (Ministry of Education); MOE-Microsoft Key Laboratory of Statistics and Information Technology of Peking University; SCI(E); ARTICLE; 1; 2499-2518; 11
语种	英语
出处	SCI
出版者	ELECTRONIC JOURNAL OF STATISTICS
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/475852]
专题	数学科学学院
推荐引用方式 GB/T 7714	Li, Yanfang,Jia, Jinzhu. L1 least squares for sparse high-dimensional LDA. 2017-01-01.

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