385 citations · 811 across the 21 of their papers we have counts for
23 papers
Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles
Tom Szwagier, Pierre-Alexandre Mattei, Charles Bouveyron +1
Gaussian mixture models (GMMs) are ubiquitous in statistical learning, particularly for unsupervised problems. While full GMMs suffer from the overparameterization of their covaria…
Generalised Mutual Information: a Framework for Discriminative Clustering
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron +4
In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regul…
The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges
Rémi Boutin, Pierre Latouche, Charles Bouveyron
Numerical interactions leading to users sharing textual content published by others are naturally represented by a network where the individuals are associated with the nodes and t…
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
Aude Sportisse, Hugo Schmutz, Olivier Humbert +2
Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of ``informative'' labels,…
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron +3
Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters. While…
Generalised Mutual Information for Discriminative Clustering
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron +4
In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regul…