Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model
This work addresses classification accuracy in high-dimensional data analysis, but it appears incremental as it builds on existing LDA methods with specific enhancements.
The paper tackled the problem of improving linear discriminant analysis for classification under spiked covariance models by proposing SRLDA, which integrates spectral correction and regularization, and showed it outperforms RLDA and ILDA in simulations and real datasets, achieving results closer to theoretical classifiers.
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 to the theoretical classifier. Experiments on different data sets show that the SRLDA algorithm performs better in classification and dimensionality reduction than currently used tools.