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In this paper, we propose an improved linear discriminant analysis, calledspectrally-corrected and regularized linear discriminant analysis (SCRLDA).This method integrates the design ideas of the sample spectrally-correctedcovariance matrix and the regularized discriminant analysis. The SCRLDA methodis specially designed for classification problems under the assumption that thecovariance matrix follows a spiked model. Through the real and simulated dataanalysis, it is shown that our proposed classifier outperforms the classicalR-LDA and can be as competitive as the KNN, SVM classifiers while requiringlower computational complexity.

Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model