Võrdle meetodeid
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| Robust Multidimensional Scaling (Robust MDS)× | Robust Cluster Analysis (TCLUST)× | |
|---|---|---|
| Valdkond | Statistika | Statistika |
| Perekond≠ | Latent structure | Regression model |
| Tekkeaasta≠ | 2002 (robust extension); 1952 (classical MDS) | 2008 |
| Looja≠ | Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952) | García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST) |
| Tüüp≠ | Dimensionality reduction / proximity scaling | Robust model-based clustering |
| Algallikas≠ | 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 ↗ | 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. DOI ↗ |
| Rööpnimetused≠ | Robust MDS, outlier-resistant MDS, robust proximity scaling | TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST) |
| Seotud≠ | 4 | 5 |
| Kokkuvõte≠ | 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. |
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