Random Matrix Theory in Pattern Classification: An Application to Error Estimation
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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. © 2013 IEEE.
author list (cited authors)
Zollanvari, A., & Dougherty, E. R.