Comparer des méthodes

	Analyse factorielle exploratoire (AFE)	Analyse en composantes principales
Domaine	Statistique	Apprentissage automatique
Famille	Latent structure	Machine learning
Année d'origine	—	2002
Auteur d'origine	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Latent variable / dimension reduction	Unsupervised dimensionality reduction
Source fondatrice	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	common factor analysis, açımlayıcı faktör analizi, factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Apparentées	4	3
Résumé	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.	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.