1. FEMDA: Une m\'ethode de classification robuste et flexible
- Author
-
Houdouin, Pierre, Jonckheere, Matthieu, and Pascal, Frederic
- Subjects
Statistics - Machine Learning ,Computer Science - Machine Learning - Abstract
Linear and Quadratic Discriminant Analysis (LDA and QDA) are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust. This paper studies the robustness to scale changes in the data of a new discriminant analysis technique where each data point is drawn by its own arbitrary Elliptically Symmetrical (ES) distribution and its own arbitrary scale parameter. Such a model allows for possibly very heterogeneous, independent but non-identically distributed samples. The new decision rule derived is simple, fast, and robust to scale changes in the data compared to other state-of-the-art method, Comment: in French language
- Published
- 2023