# Robust classification with flexible discriminant analysis in heterogeneous data

arXiv: https://arxiv.org/abs/2201.02967 · HTML: https://mlsanity.com/paper/2201.02967/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 9 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [A flexible EM-like clustering algorithm for noisy data](https://mlsanity.com/paper/1907.01660/) (2019)
- [Robust Generalised Quadratic Discriminant Analysis](https://mlsanity.com/paper/2004.06568/) (2020)

## Built on by (most cited first, 5 of 5)

- [FEMDA: Une m'ethode de classification robuste et flexible](https://mlsanity.com/paper/2307.01954/) (2023)
- [Regularized EM algorithm](https://mlsanity.com/paper/2303.14989/) (2023)
- [Algorithme EM r'egularis'e](https://mlsanity.com/paper/2307.01955/) (2023)
- [FEMDA: a unified framework for discriminant analysis](https://mlsanity.com/paper/2311.07518/) (2023)
- [Convex Parameter Estimation of Perturbed Multivariate Generalized Gaussian Distributions](https://mlsanity.com/paper/2312.07479/) (2023)

## Abstract (arXiv)

Linear and Quadratic Discriminant Analysis 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. To fill this gap, this paper presents a new robust discriminant analysis 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. After deriving a new decision rule, it is shown that maximum-likelihood parameter estimation and classification are very simple, fast and robust compared to state-of-the-art methods.