论文标题
峰值校正和正则线性判别分析，用于峰值协方差模型
Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model
论文作者
论文摘要
本文提出了改进的线性判别分析，称为频谱校正和正则化LDA（SRLDA）。该方法集成了样品频谱校正的协方差矩阵和正则判别分析的设计思想。在一个大维随机矩阵分析框架的支持下，证明SRLDA在加标模型假设下具有线性分类全局最佳解决方案。根据仿真数据分析，SRLDA分类器的性能优于RLDA和ILDA，并且更接近理论分类器。不同数据集的实验表明，与当前使用的工具相比，SRLDA算法在分类和尺寸降低方面的性能更好。
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.
