Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Diskriminační analýza× | Kanonická korelační analýza× | |
|---|---|---|
| Obor | Statistika | Statistika |
| Rodina | Latent structure | Latent structure |
| Rok vzniku | 1936 | 1936 |
| Tvůrce≠ | Ronald A. Fisher | Harold Hotelling |
| Typ≠ | Supervised classification and dimension reduction | Multivariate linear dimension reduction and association |
| Původní zdroj≠ | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ | Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377. DOI ↗ |
| Další názvy | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis | CCA, canonical variate analysis, canonical analysis, multiple canonical correlation |
| Příbuzné | 4 | 4 |
| Shrnutí≠ | 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. |
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