Comparar métodos

	Análisis Discriminante Lineal (LDA	Análisis de Componentes Principales
Campo	Estadística	Aprendizaje automático
Familia	Hypothesis test	Machine learning
Año de origen	1936	2002
Autor original	Ronald A. Fisher	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipo	Parametric linear classifier / dimensionality reduction	Unsupervised dimensionality reduction
Fuente seminal	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.
Alias	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Relacionados	7	3
Resumen	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.