विधियों की तुलना करें

	सुदृढ़ पत्राचार विश्लेषण	रोबस्ट मल्टीडायमेंशनल स्केलिंग (रोबस्ट एमडीएस)
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Latent structure	Latent structure
उद्भव वर्ष	2000s (robust extensions of CA developed since the early 2000s)	2002 (robust extension); 1952 (classical MDS)
प्रवर्तक	Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues	Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952)
प्रकार	Robust dimension reduction for contingency tables	Dimensionality reduction / proximity scaling
मौलिक स्रोत	Croux, C. & Ruiz-Gazen, A. (2005). High breakdown estimators for principal components: the projection-pursuit approach revisited. Journal of Multivariate Analysis, 95(1), 206–226.	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.
उपनाम	RCA, outlier-resistant correspondence analysis, robust CA	Robust MDS, outlier-resistant MDS, robust proximity scaling
संबंधित	5	4
सारांश	Robust Correspondence Analysis (RCA) extends classical correspondence analysis to contingency tables that contain outlying rows or columns. By replacing the standard singular value decomposition with a robust alternative, RCA produces biplots and coordinate maps that accurately reflect the dominant association structure even when atypical cells or categories exert undue influence on the standard solution.	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.