方法对比
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| 典型相关分析× | 判别分析× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份 | 1936 | 1936 |
| 提出者≠ | Harold Hotelling | Ronald A. Fisher |
| 类型≠ | Multivariate linear dimension reduction and association | Supervised classification and dimension reduction |
| 开创性文献≠ | 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 ↗ |
| 别名 | CCA, canonical variate analysis, canonical analysis, multiple canonical correlation | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| 相关 | 4 | 4 |
| 摘要≠ | 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. |
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