Порівняння методів

	Робастний факторний аналіз	Метод головних компонент	Оцінювання робастної коваріації (MCD)
Галузь	Статистика	Машинне навчання	Статистика
Родина	Regression model	Machine learning	Regression model
Рік появи	2003	2002	1999
Автор методу	Pison, Rousseeuw, Filzmoser & Croux	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Тип	Robust latent-factor model	Unsupervised dimensionality reduction	Robust multivariate location-scatter estimator
Основоположне джерело	Pison, G., Rousseeuw, P. J., Filzmoser, P., & Croux, C. (2003). Robust factor analysis. Journal of Multivariate Analysis, 84(1), 145-172.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Інші назви	robust factor analysis, outlier-resistant factor analysis, MCD-based factor analysis, Robust Faktör Analizi	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Пов'язані	5	3	4
Підсумок	Robust Factor Analysis recovers the latent factor structure of multivariate continuous data while resisting the distorting pull of outliers. Introduced by Pison, Rousseeuw, Filzmoser and Croux (2003), it replaces the classical sample covariance with a robust estimator such as the Minimum Covariance Determinant (MCD) or an S-estimator before extracting factors.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.	Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation.