方法证据记录
Robust Discriminant Analysis
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).
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
High-Breakdown Robust Linear Discriminant Analysis
分类方法记录 · regression-model / statistics
- Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143. · DOI 10.1080/01621459.1997.10473610
- Croux, C. & Dehon, C. (2001). Robust Linear Discriminant Analysis Using S-Estimators. Canadian Journal of Statistics, 29(3), 473-493. · DOI 10.2307/3316042
精选声明
声明已持久化到证据分类账中，每个声明都有自己的评估。
尚无精选声明
当分类账中没有声明时，此视图不会自行创建声明评估。
相关方法
从方法图中生成，显示为机器建议的关系 — 不推断任何证据声明。