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

arXiv: https://arxiv.org/abs/2307.01954 · HTML: https://mlsanity.com/paper/2307.01954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [A flexible EM-like clustering algorithm for noisy data](https://mlsanity.com/paper/1907.01660/) (2019)
- [Robust classification with flexible discriminant analysis in heterogeneous data](https://mlsanity.com/paper/2201.02967/) (2022)
- [Robust Generalised Quadratic Discriminant Analysis](https://mlsanity.com/paper/2004.06568/) (2020)

## Built on by (most cited first, 1 of 1)

- [Exponential Family Discriminant Analysis: Generalizing LDA-Style Generative Classification to Non-Gaussian Models](https://mlsanity.com/paper/2603.20655/) (2026)

## Abstract (arXiv)

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