Methoden vergleichen

	Lineare Diskriminanzanalyse (LDA	Hauptkomponentenanalyse
Fachgebiet	Statistik	Maschinelles Lernen
Familie	Hypothesis test	Machine learning
Entstehungsjahr	1936	2002
Urheber	Ronald A. Fisher	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Parametric linear classifier / dimensionality reduction	Unsupervised dimensionality reduction
Wegweisende Quelle	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.
Aliasnamen	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Verwandt	7	3
Zusammenfassung	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.