FEMDA: Une méthode de classification robuste et flexible
Pierre Houdouin Matthieu Jonckheere Frederic Pascal
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