विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| रोबस्ट मल्टीडायमेंशनल स्केलिंग (रोबस्ट एमडीएस)× | मल्टीडायमेंशनल स्केलिंग (MDS)× | |
|---|---|---|
| क्षेत्र | सांख्यिकी | सांख्यिकी |
| परिवार | Latent structure | Latent structure |
| उद्भव वर्ष≠ | 2002 (robust extension); 1952 (classical MDS) | 1952–1964 |
| प्रवर्तक≠ | Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952) | Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964) |
| प्रकार≠ | Dimensionality reduction / proximity scaling | Dimensionality reduction / visualization |
| मौलिक स्रोत≠ | 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. link ↗ | Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27. DOI ↗ |
| उपनाम≠ | Robust MDS, outlier-resistant MDS, robust proximity scaling | MDS, metric MDS, non-metric MDS, proximity scaling |
| संबंधित≠ | 4 | 5 |
| सारांश≠ | 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. |
| ScholarGateडेटासेट ↗ |
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