Application of Double Asymptotics and Random Matrix Theory in Error Estimation of Regularized Linear Discriminant Analysis

被引：0

作者：

Zollanvari, Amin ^{[1
,2
]}

Dougherty, Edward R. ^{[1
,3
,4
]}

机构：

[1] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA

[2] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA

[3] Translat Genom Res Inst, Phoenix, AZ USA

[4] Univ Texas MD Anderson Canc Ctr, Dept Bioinformat & Computat Biol, Houston, TX 77030 USA

来源：

2013 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | 2013年

关键词：

Linear discriminant analysis; Small-Sample; Double asymptotics; Kolmogorov asymptotics; Random matrix theory;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The theory of double asymptotics and random matrices has been employed to construct a nearly unbiased estimator of true error rate of linear discriminant analysis with ridge estimator of inverse covariance matrix in the multivariate Gaussian model. In such a scenario, the performance of the constructed estimator, as measured by Root-Mean-Square (RMS) error, shows improvement over well-known estimators of true error.

引用

页码：57 / 59

页数：3

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