方法对比
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| 多维尺度分析 (MDS)× | 判别分析× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1952–1964 | 1936 |
| 提出者≠ | Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964) | Ronald A. Fisher |
| 类型≠ | Dimensionality reduction / visualization | Supervised classification and dimension reduction |
| 开创性文献≠ | Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27. DOI ↗ | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| 别名 | MDS, metric MDS, non-metric MDS, proximity scaling | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| 相关≠ | 5 | 4 |
| 摘要≠ | Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data. | 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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