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

10/08/2022
by   Hua Li, et al.
0

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

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset