方法对比

	稳健多维尺度分析 (Robust MDS)	多维尺度分析 (MDS)
领域	统计学	统计学
方法族	Latent structure	Latent structure
起源年份	2002 (robust extension); 1952 (classical MDS)	1952–1964
提出者	Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952)	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)
类型	Dimensionality reduction / proximity scaling	Dimensionality reduction / visualization
开创性文献	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.	Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.
别名	Robust MDS, outlier-resistant MDS, robust proximity scaling	MDS, metric MDS, non-metric MDS, proximity scaling
相关	4	5
摘要	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.	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.