Salīdzināt metodes

	Robustā daudzdimensiju skalēšana (Robust MDS)	Multidimensionālā skalēšana (MDS)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	2002 (robust extension); 1952 (classical MDS)	1952–1964
Autors	Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952)	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)
Tips	Dimensionality reduction / proximity scaling	Dimensionality reduction / visualization
Pirmavots	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.
Citi nosaukumi	Robust MDS, outlier-resistant MDS, robust proximity scaling	MDS, metric MDS, non-metric MDS, proximity scaling
Saistītās	4	5
Kopsavilkums	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.