Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise Discriminante Robusta× | Análise Discriminante Linear (LDA)× | |
|---|---|---|
| Área≠ | Estatística | Aprendizado de máquina |
| Família≠ | Regression model | Latent structure |
| Ano de origem≠ | 1997 | 1936 |
| Autor original≠ | Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA) | Fisher, R. A. |
| Tipo≠ | Robust classification / discriminant analysis | Supervised dimensionality reduction and linear classifier |
| Fonte seminal≠ | Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143. DOI ↗ | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| Outros nomes≠ | robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi | LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis |
| Relacionados≠ | 5 | 4 |
| Resumo≠ | Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001). | Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning. |
| ScholarGateConjunto de dados ↗ |
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