Сравнение методов

	Многомерное шкалирование (MDS)	Дискриминантный анализ
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	1952–1964	1936
Автор метода	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
Основополагающий источник	Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Другие названия	MDS, metric MDS, non-metric MDS, proximity scaling	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
Связанные	5	4
Сводка	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.