手法を比較

	Canonical Correlation Analysis	判別分析
分野	統計学	統計学
系統	Latent structure	Latent structure
提唱年	1936	1936
提唱者	Harold Hotelling	Ronald A. Fisher
種類	Multivariate linear dimension reduction and association	Supervised classification and dimension reduction
原典	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.
別名	CCA, canonical variate analysis, canonical analysis, multiple canonical correlation	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
関連	4	4
概要	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.