방법 비교

	선형 판별 분석 (LDA	주성분 분석
분야	통계학	머신러닝
계열	Hypothesis test	Machine learning
기원 연도	1936	2002
창시자	Ronald A. Fisher	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
유형	Parametric linear classifier / dimensionality reduction	Unsupervised dimensionality reduction
원전	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
별칭	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
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.	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.