Robust Classification with Flexible Discriminant Analysis in Heterogeneous Data

Pierre Houdouin; Andrew Wang; Matthieu Jonckheere; Frédéric Pascal

ICASSP 2022accepted0 citations

Robust Classification with Flexible Discriminant Analysis in Heterogeneous Data

Pierre Houdouin, Andrew Wang, Matthieu Jonckheere, Frédéric Pascal

Abstract

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.

BibTeX

@inproceedings{icassp2022_robustclassifica,
  title = {Robust Classification with Flexible Discriminant Analysis in Heterogeneous Data},
  author = {Pierre Houdouin and Andrew Wang and Matthieu Jonckheere and Frédéric Pascal},
  booktitle = {ICASSP 2022},
  year = {2022}
}