Salīdzināt metodes

	Robustā daudzdimensiju skalēšana (Robust MDS)	Robustā kopu analīze (TCLUST)
Nozare	Statistika	Statistika
Saime	Latent structure	Regression model
Izcelsmes gads	2002 (robust extension); 1952 (classical MDS)	2008
Autors	Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952)	García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST)
Tips	Dimensionality reduction / proximity scaling	Robust model-based clustering
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.	García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A General Trimming Approach to Robust Cluster Analysis. The Annals of Statistics, 36(3), 1324-1345.
Citi nosaukumi	Robust MDS, outlier-resistant MDS, robust proximity scaling	TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST)
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.	Robust Cluster Analysis is a trimmed model-based clustering method, introduced by García-Escudero and colleagues in 2008, that partitions continuous multivariate data into clusters while resisting the influence of outliers and noise. By setting aside a fraction of the most discordant observations, it keeps the recovered cluster structure from being contaminated by stray points.