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
收藏
页码：57 / 59
页数：3
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