Comparer des méthodes

	Analyse factorielle robuste	Analyse en composantes principales
Domaine	Statistique	Apprentissage automatique
Famille	Regression model	Machine learning
Année d'origine	2003	2002
Auteur d'origine	Pison, Rousseeuw, Filzmoser & Croux	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Robust latent-factor model	Unsupervised dimensionality reduction
Source fondatrice	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.
Alias	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
Apparentées	5	3
Résumé	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.