143 citations · 522 across the 25 of their papers we have counts for
30 papers
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…
Affine equivariant Tyler's M-estimator applied to tail parameter learning of elliptical distributions
Esa Ollila, Daniel P. Palomar, Frederic Pascal
We propose estimating the scale parameter (mean of the eigenvalues) of the scatter matrix of an unspecified elliptically symmetric distribution using weights obtained by solving Ty…
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…
A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data
Florian Mouret, Alexandre Hippert-Ferrer, Frédéric Pascal +1
This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A classical imputation method, the Expectation Maximization (EM) algorithm for Gaussian m…