Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise de Cluster Robusta (TCLUST)× | Análise de Componentes Principais Robusta (RPCA)× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2008 | 2011 |
| Autor original≠ | García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST) | Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005) |
| Tipo≠ | Robust model-based clustering | Robust dimensionality reduction / matrix decomposition |
| Fonte seminal≠ | 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 ↗ | Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37. DOI ↗ |
| Outros nomes | TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST) | RPCA, robust principal component analysis, low-rank plus sparse decomposition, Robust Temel Bileşen Analizi (RPCA) |
| Relacionados≠ | 5 | 3 |
| Resumo≠ | 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. | Robust Principal Component Analysis is a dimensionality-reduction method that extracts reliable components when the data are contaminated by outliers and noise. Introduced by Candès, Li, Ma and Wright (2011), and developed in the ROBPCA approach of Hubert, Rousseeuw and Vanden Branden (2005), it separates a data matrix into a clean low-rank part and a sparse outlier part. |
| ScholarGateConjunto de dados ↗ |
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