Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Багатовимірне шкалування (MDS)× | Дискримінантний аналіз× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Latent structure | Latent structure |
| Рік появи≠ | 1952–1964 | 1936 |
| Автор методу≠ | Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964) | Ronald A. Fisher |
| Тип≠ | Dimensionality reduction / visualization | Supervised classification and dimension reduction |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | MDS, metric MDS, non-metric MDS, proximity scaling | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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