1 citations · 1 across the 5 of their papers we have counts for
5 papers
FEMDA: a unified framework for discriminant analysis
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…
Algorithme EM régularisé
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). When the samp…
FEMDA: Une méthode de classification robuste et flexible
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, mainl…
Regularized EM algorithm
Pierre Houdouin, Esa Ollila, Frederic Pascal
Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing (local) maximum likelihood estimate (MLE). It can be used in an extensive range of proble…
Robust classification with flexible discriminant analysis in heterogeneous data
Pierre Houdouin, Frédéric Pascal, Matthieu Jonckheere +1
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 t…