Application Of Double Asymptotics and Random Matrix Theory in Error Estimation of Regularized Linear Discriminant Analysis
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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. © 2013 IEEE.
author list (cited authors)
Zollanvari, A., & Dougherty, E. R.