Title :
Random matrix theory in pattern classification: An application to error estimation
Author :
Zollanvari, Amin ; Dougherty, Edward
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
We employed the Random Matrix Theory (RMT) to construct a nearly unbiased estimator of true error rate of linear discriminant analysis (LDA) with ridge estimator of inverse covariance matrix in the multivariate Gaussian model and in small-sample situation. In such a scenario, the performance of the constructed estimator, as measured by Root-Mean-Square (RMS) error, shows consistent improvement over well-known estimators of true error.
Keywords :
Gaussian distribution; covariance matrices; estimation theory; mean square error methods; pattern classification; signal classification; LDA; RMS error; RMT; error estimation; error rate; inverse covariance matrix; linear discriminant analysis; multivariate Gaussian model; nearly unbiased estimator; pattern classification; random matrix theory; ridge estimator; root mean square error; small-sample situation; Biological system modeling; Computers; Covariance matrices; Error analysis; Linear discriminant analysis; Plugs; Standards; Error Estimation; Linear Discriminant Analysis; Linear discriminant analysis; Small-Sample;
Conference_Titel :
Signals, Systems and Computers, 2013 Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4799-2388-5
DOI :
10.1109/ACSSC.2013.6810415
