Сравнение на методи

	Каноничен корелационен анализ	Дискриминантен анализ
Област	Статистика	Статистика
Семейство	Latent structure	Latent structure
Година на възникване	1936	1936
Създател	Harold Hotelling	Ronald A. Fisher
Тип	Multivariate linear dimension reduction and association	Supervised classification and dimension reduction
Основополагащ източник	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.
Други названия	CCA, canonical variate analysis, canonical analysis, multiple canonical correlation	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
Свързани	4	4
Резюме	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.