Сравнение методов

	Дискриминантный анализ	Канонический корреляционный анализ
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	1936	1936
Автор метода	Ronald A. Fisher	Harold Hotelling
Тип	Supervised classification and dimension reduction	Multivariate linear dimension reduction and association
Основополагающий источник	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.	Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377.
Другие названия	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis	CCA, canonical variate analysis, canonical analysis, multiple canonical correlation
Связанные	4	4
Сводка	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.	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.