Latent structureMultivariate analysis

Robust Multidimensional Scaling (Robust MDS)

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.

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Sources

  1. 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. DOI: 10.1007/s00357-002-0010-0
  2. Buja, A., Swayne, D. F., Littman, M. L., Dean, N., Hofmann, H. & Chen, L. (2008). Data visualization with multidimensional scaling. Journal of Computational and Graphical Statistics, 17(2), 444–472. DOI: 10.1198/106186008X318440

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Referenced by

ScholarGateRobust Multidimensional Scaling (Robust Multidimensional Scaling). Retrieved 2026-06-04 from https://scholargate.app/en/statistics/robust-multidimensional-scaling