Сравнение методов

	Робастное многомерное шкалирование (Robust MDS)	Робастный анализ соответствий
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	2002 (robust extension); 1952 (classical MDS)	2000s (robust extensions of CA developed since the early 2000s)
Автор метода	Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952)	Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues
Тип	Dimensionality reduction / proximity scaling	Robust dimension reduction for contingency tables
Основополагающий источник	Hubert, L., Arabie, P. & Meulman, J. (2002). Linear unidimensional scaling in the L2-norm: Basic optimization methods using SMACOF. Journal of Classification, 19(2), 303–327.	Croux, C. & Ruiz-Gazen, A. (2005). High breakdown estimators for principal components: the projection-pursuit approach revisited. Journal of Multivariate Analysis, 95(1), 206–226.
Другие названия	Robust MDS, outlier-resistant MDS, robust proximity scaling	RCA, outlier-resistant correspondence analysis, robust CA
Связанные	4	5
Сводка	Robust multidimensional scaling recovers a low-dimensional spatial map from a matrix of pairwise dissimilarities while resisting distortion caused by outlying or erroneous proximity values. By replacing squared-error loss with a robust loss function or down-weighting suspect pairs, it produces a configuration that faithfully represents the bulk of the data even when some distances are grossly atypical.	Robust Correspondence Analysis (RCA) extends classical correspondence analysis to contingency tables that contain outlying rows or columns. By replacing the standard singular value decomposition with a robust alternative, RCA produces biplots and coordinate maps that accurately reflect the dominant association structure even when atypical cells or categories exert undue influence on the standard solution.