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| 稳健判别分析× | 二次判别分析 (QDA)× | |
|---|---|---|
| 领域≠ | 统计学 | 机器学习 |
| 方法族≠ | Regression model | Latent structure |
| 起源年份≠ | 1997 | 1939 |
| 提出者≠ | Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA) | Classical Gaussian discriminant analysis (Fisher / Welch lineage) |
| 类型≠ | Robust classification / discriminant analysis | Generative Gaussian classifier |
| 开创性文献≠ | Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143. DOI ↗ | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0 |
| 别名≠ | robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi | QDA, quadratic classifier, kuadratik diskriminant analizi |
| 相关≠ | 5 | 2 |
| 摘要≠ | 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). | Quadratic discriminant analysis is a generative classifier that models each class with its own multivariate Gaussian distribution, allowing each class a separate covariance matrix. Unlike linear discriminant analysis, which assumes a shared covariance and yields linear boundaries, QDA's per-class covariances produce curved (quadratic) decision boundaries, letting it capture differences in the spread and orientation of the classes. |
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