Сравнение методов

	Робастный канонический корреляционный анализ (Robust CCA)	Робастное многомерное шкалирование (Robust MDS)
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	2003	2002 (robust extension); 1952 (classical MDS)
Автор метода	Croux & Dehon (building on Hotelling's CCA framework)	Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952)
Тип	Robust multivariate association	Dimensionality reduction / proximity scaling
Основополагающий источник	Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569.	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.
Другие названия	Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation	Robust MDS, outlier-resistant MDS, robust proximity scaling
Связанные	4	4
Сводка	Robust canonical correlation analysis extends classical CCA by replacing the standard sample covariance matrix with a robust estimator — such as the Minimum Covariance Determinant (MCD) or S-estimator — so that outlying observations do not distort the estimated canonical correlations and canonical variates between two sets of variables.	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.