4 citations · 4 across the 15 of their papers we have counts for
9 papers · 1 filter
Personalised federated learning for Riemannian and Euclidean EEG decoding
Thibault Pautrel, Florent Bouchard, Ammar Mian +1
Federated learning (FL) lets EEG decoders learn from recordings of several subjects without pooling them. We consider two light EEG decoders, the Riemannian SPDNet and the Euclidea…
FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
Thibault Pautrel, Florent Bouchard, Ammar Mian +1
We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with Stiefel-constrained parameters. Unlike st…
Beyond R-barycenters: an effective averaging method on Stiefel and Grassmann manifolds
Florent Bouchard, Nils Laurent, Salem Said +1
In this paper, the issue of averaging data on a manifold is addressed. While the Fréchet mean resulting from Riemannian geometry appears ideal, it is unfortunately not always avail…
Elliptical Wishart distributions: information geometry, maximum likelihood estimator, performance analysis and statistical learning
Imen Ayadi, Florent Bouchard, Frédéric Pascal
This paper deals with Elliptical Wishart distributions - which generalize the Wishart distribution - in the context of signal processing and machine learning. Two algorithms to com…
Random matrix theory improved Fréchet mean of symmetric positive definite matrices
Florent Bouchard, Ammar Mian, Malik Tiomoko +2
In this study, we consider the realm of covariance matrices in machine learning, particularly focusing on computing Fréchet means on the manifold of symmetric positive definite mat…
The Fisher-Rao geometry of CES distributions
Florent Bouchard, Arnaud Breloy, Antoine Collas +2
When dealing with a parametric statistical model, a Riemannian manifold can naturally appear by endowing the parameter space with the Fisher information metric. The geometry induce…
Learning Graphical Factor Models with Riemannian Optimization
Alexandre Hippert-Ferrer, Florent Bouchard, Ammar Mian +2
Graphical models and factor analysis are well-established tools in multivariate statistics. While these models can be both linked to structures exhibited by covariance and precisio…
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection
Florent Bouchard, Ammar Mian, Jialun Zhou +3
A new Riemannian geometry for the Compound Gaussian distribution is proposed. In particular, the Fisher information metric is obtained, along with corresponding geodesics and dista…
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Malik Tiomoko, Florent Bouchard, Guillaume Ginholac +1
Relying on recent advances in statistical estimation of covariance distances based on random matrix theory, this article proposes an improved covariance and precision matrix estima…