Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão RANSAC× | Regressão por Mínimos Quadrados Truncados (LTS)× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1981 | 1984 |
| Autor original≠ | Fischler & Bolles | Peter J. Rousseeuw |
| Tipo | Robust linear regression | Robust linear regression |
| Fonte seminal≠ | Fischler, M. A. & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24(6), 381-395. DOI ↗ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ |
| Outros nomes≠ | random sample consensus, RANSAC, robust regression, RANSAC Regresyonu | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| Relacionados | 5 | 5 |
| Resumo≠ | RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeatedly samples small subsets, fits a candidate model, and keeps the model that wins the largest consensus of agreeing points. | Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers. |
| ScholarGateConjunto de dados ↗ |
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