方法对比

	稳健判别分析	线性判别分析 (LDA)
领域	统计学	机器学习
方法族	Regression model	Latent structure
起源年份	1997	1936
提出者	Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)	Fisher, R. A.
类型	Robust classification / discriminant analysis	Supervised dimensionality reduction and linear classifier
开创性文献	Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143.	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
别名	robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis
相关	5	4
摘要	Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001).	Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.