Salīdzināt metodes

	Kanonsakņu korelācijas analīze	Lineārā diskriminantā analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1936	1936
Autors	Harold Hotelling	Ronald A. Fisher
Tips	Multivariate linear dimension reduction and association	Supervised classification and dimension reduction
Pirmavots	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.
Citi nosaukumi	CCA, canonical variate analysis, canonical analysis, multiple canonical correlation	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
Saistītās	4	4
Kopsavilkums	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.