方法对比

	稳健典型相关分析 (Robust CCA)	稳健多维尺度分析 (Robust MDS)
领域	统计学	统计学
方法族	Latent structure	Latent structure
起源年份	2003	2002 (robust extension); 1952 (classical MDS)
提出者	Croux & Dehon (building on Hotelling's CCA framework)	Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952)
类型	Robust multivariate association	Dimensionality reduction / proximity scaling
开创性文献	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.
别名	Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation	Robust MDS, outlier-resistant MDS, robust proximity scaling
相关	4	4
摘要	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.