Comparer des méthodes

	Analyse en composantes principales	Estimation Robuste de la Covariance (MCD)
Domaine	Apprentissage automatique	Statistique
Famille	Machine learning	Regression model
Année d'origine	2002	1999
Auteur d'origine	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Type	Unsupervised dimensionality reduction	Robust multivariate location-scatter estimator
Source fondatrice	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.
Alias	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Apparentées	3	4
Résumé	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.