Compară metode

	Analiza de corelație canonică	Analiza discriminantă
Domeniu	Statistică	Statistică
Familie	Latent structure	Latent structure
Anul apariției	1936	1936
Autorul original	Harold Hotelling	Ronald A. Fisher
Tip	Multivariate linear dimension reduction and association	Supervised classification and dimension reduction
Sursa seminală	Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377.	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Denumiri alternative	CCA, canonical variate analysis, canonical analysis, multiple canonical correlation	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
Înrudite	4	4
Rezumat	Canonical Correlation Analysis (CCA) is a multivariate statistical method that identifies pairs of linear combinations — one from each of two variable sets — such that the correlation between each pair is maximised. Introduced by Harold Hotelling in his landmark 1936 Biometrika paper, CCA provides the most general linear framework for studying the association between two multivariate batteries of measurements, and many classical procedures (multiple regression, MANOVA, discriminant analysis) are special cases of it.	Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error.