FEMDA: Une m\'ethode de classification robuste et flexible

• URL: http://arxiv.org/abs/2307.01954v1
• Date: Tue, 4 Jul 2023 23:15:31 GMT
• Title: FEMDA: Une m\'ethode de classification robuste et flexible
• Authors: Pierre Houdouin and Matthieu Jonckheere and Frederic Pascal
• Abstract summary: This paper studies robustness to scale changes in the data of a new discriminant analysis technique. The new decision rule derived is simple, fast, and robust to scale changes in the data compared to other state-of-the-art method.
• Score: 0.8594140167290096
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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

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