Salīdzināt metodes

	Eksploratīvā faktoru analīze (EFA)	Primārā komponentu analīze
Nozare	Statistika	Mašīnmācīšanās
Saime	Latent structure	Machine learning
Izcelsmes gads	—	2002
Autors	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tips	Latent variable / dimension reduction	Unsupervised dimensionality reduction
Pirmavots	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.
Citi nosaukumi	common factor analysis, açımlayıcı faktör analizi, factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Saistītās	4	3
Kopsavilkums	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.