Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise de Correlação Canônica× | Análise Discriminante× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Latent structure | Latent structure |
| Ano de origem | 1936 | 1936 |
| Autor original≠ | Harold Hotelling | Ronald A. Fisher |
| Tipo≠ | Multivariate linear dimension reduction and association | Supervised classification and dimension reduction |
| Fonte seminal≠ | Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377. DOI ↗ | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| Outros nomes | CCA, canonical variate analysis, canonical analysis, multiple canonical correlation | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| Relacionados | 4 | 4 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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