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

arXiv:2210.03859v3 Announce Type: replace-cross
Abstract: This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA). This method integrates the design ideas of the sample spectrally-corrected covariance matrix and the regularized discriminant analysis. With the support of a large-dimensional random matrix analysis framework, it is proved that SRLDA has a linear classification global optimal solution under the spiked model assumption. According to simulation data analysis, the SRLDA classifier performs better than RLDA and ILDA and is closer …

abstract analysis arxiv covariance cs.lg design framework ideas lda linear matrix paper random sample stat.ml support type