Spectrally-Corrected and Regularized LDA for Spiked Model.

This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA). This approach incorporates design principles from both the spectrally-corrected covariance matrix and the regularized discriminant analysis. With the support of a large-dimensional random matrix theory, it is demonstrated that SRLDA achieves a globally optimal linear classification solution under the spiked model assumption.