Title :
Application of double asymptotics and random matrix theory in error estimation of regularized linear discriminant analysis
Author :
Zollanvari, Amin ; Dougherty, Edward
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
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.
Keywords :
Gaussian processes; covariance matrices; mean square error methods; statistical analysis; RMS error; double asymptotics theory; error estimation; inverse covariance matrix; multivariate Gaussian model; nearly unbiased estimator; random matrix theory; regularized linear discriminant analysis; ridge estimator; root-mean-square; Measurement; Protocols; Double asymptotics; Kolmogorov asymptotics; Linear discriminant analysis; Random matrix theory; Small-Sample;
Conference_Titel :
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location :
Austin, TX
DOI :
10.1109/GlobalSIP.2013.6736811
