Порівняння методів

	Робастний дискримінантний аналіз	Квадратичний дискримінантний аналіз (QDA)
Галузь	Статистика	Машинне навчання
Родина	Regression model	Latent structure
Рік появи	1997	1939
Автор методу	Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)	Classical Gaussian discriminant analysis (Fisher / Welch lineage)
Тип	Robust classification / discriminant analysis	Generative Gaussian classifier
Основоположне джерело	Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143.	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0
Інші назви	robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi	QDA, quadratic classifier, kuadratik diskriminant analizi
Пов'язані	5	2
Підсумок	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).	Quadratic discriminant analysis is a generative classifier that models each class with its own multivariate Gaussian distribution, allowing each class a separate covariance matrix. Unlike linear discriminant analysis, which assumes a shared covariance and yields linear boundaries, QDA's per-class covariances produce curved (quadratic) decision boundaries, letting it capture differences in the spread and orientation of the classes.