방법 비교

	선형 판별 분석 (LDA	요인 분석
분야	통계학	연구 통계
계열	Hypothesis test	Process / pipeline
기원 연도	1936	1931
창시자	Ronald A. Fisher	Louis Leon Thurstone
유형	Parametric linear classifier / dimensionality reduction	Method
원전	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.
별칭	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	EFA, CFA, latent variable modeling
관련	7	3
요약	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	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.