Comparer des méthodes

	Analyse factorielle robuste	Analyse en Composantes Principales Robuste (RPCA)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2003	2011
Auteur d'origine	Pison, Rousseeuw, Filzmoser & Croux	Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)
Type	Robust latent-factor model	Robust dimensionality reduction / matrix decomposition
Source fondatrice	Pison, G., Rousseeuw, P. J., Filzmoser, P., & Croux, C. (2003). Robust factor analysis. Journal of Multivariate Analysis, 84(1), 145-172.	Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37.
Alias	robust factor analysis, outlier-resistant factor analysis, MCD-based factor analysis, Robust Faktör Analizi	RPCA, robust principal component analysis, low-rank plus sparse decomposition, Robust Temel Bileşen Analizi (RPCA)
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.	Robust Principal Component Analysis is a dimensionality-reduction method that extracts reliable components when the data are contaminated by outliers and noise. Introduced by Candès, Li, Ma and Wright (2011), and developed in the ROBPCA approach of Hubert, Rousseeuw and Vanden Branden (2005), it separates a data matrix into a clean low-rank part and a sparse outlier part.