השוואת שיטות

	ניתוח רכיבים עיקריים	ניתוח גורמים
תחום	למידת מכונה	סטטיסטיקה למחקר
משפחה	Machine learning	Process / pipeline
שנת המקור	2002	1931
הוגה השיטה	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Louis Leon Thurstone
סוג	Unsupervised dimensionality reduction	Method
מקור מכונן	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.
כינויים	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	EFA, CFA, latent variable modeling
קשורות	3	3
תקציר	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.	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.