השוואת שיטות

	ניתוח גורמים גישוש (EFA)	ניתוח רכיבים עיקריים
תחום	סטטיסטיקה	למידת מכונה
משפחה	Latent structure	Machine learning
שנת המקור	—	2002
הוגה השיטה	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
סוג	Latent variable / dimension reduction	Unsupervised dimensionality reduction
מקור מכונן	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.
כינויים	common factor analysis, açımlayıcı faktör analizi, factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
קשורות	4	3
תקציר	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.