no code implementations • 13 Nov 2023 • Pierre Houdouin, Matthieu Jonckheere, Frederic Pascal
Although linear and quadratic discriminant analysis are widely recognized classical methods, they can encounter significant challenges when dealing with non-Gaussian distributions or contaminated datasets.
no code implementations • 4 Jul 2023 • Pierre Houdouin, Matthieu Jonkcheere, Frederic Pascal
Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing maximum likelihood estimate when dealing with Gaussian Mixture Model (GMM).
no code implementations • 4 Jul 2023 • 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.
no code implementations • 27 Mar 2023 • Pierre Houdouin, Esa Ollila, Frederic Pascal
We show that the theoretical guarantees of convergence hold, leading to better performing EM algorithm for structured covariance matrix models or with low sample settings.
1 code implementation • 9 Jan 2022 • Pierre Houdouin, Frédéric Pascal, Matthieu Jonckheere, Andrew Wang
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.