L2S, CentraleSupélec
Bât. Breguet C4.13A
3 rue Joliot Curie
91190 Gif-sur-Yvette, France

+33 (0)1 69 85 17 31




Probabilistic PCA From Heteroscedastic Signals: Geometric Framework and Application to Clustering

Collas, Bouchard, Breloy, Ginolhac, Ren, Ovarlez
IEEE Transactions on Signal Processing, 69, p. 6546-6560, 2021

A Riemannian Framework for Low-Rank Structured Elliptical Models

Bouchard, Breloy, Ginolhac, Renaux, Pascal
IEEE Transactions on Signal Processing, 69, p. 1185-1199, 2021

Riemannian geometry for compound Gaussian distributions: Application to recursive change detection

Bouchard, Mian, Zhou, Said, Ginolhac, Berthoumieu
Signal process., 176, Elsevier, p. 107716, 2020

Approximate joint diagonalization with Riemannian optimization on the general linear group

Bouchard, Afsari, Malick, Congedo
SIAM j. matrix anal. appl., 41 (1), Society for Industrial and Applied Mathematics, p. 152-170, 2020

Intrinsic Cramér–Rao bounds for scatter and shape matrices estimation in CES distributions

Breloy, Ginolhac, Renaux, Bouchard
IEEE Signal Processing Letters, 26 (2), p. 262-266, 2019

Riemannian Optimization and Approximate Joint Diagonalization for Blind Source Separation

Bouchard, Malick, Congedo
IEEE Transactions on Signal Processing, 66 (8), p. 2041-2054, 2018



On-line Kronecker Product Structured Covariance Estimation with Riemannian geometry for t-distributed data

Bouchard, Breloy, Mian, Ginolhac
2021 29th European Signal Processing Conference (EUSIPCO), Dublin, Irlande, 2021

A Riemannian approach to blind separation of t-distributed sources

Bouchard, Breloy, Ginolhac, Renaux
EUSIPCO 2020, Amsterdam, France, 2020

Riemannian framework for robust covariance matrix estimation in spiked models

Bouchard, Breloy, Ginolhac, Pascal
IEEE ICASSP 2020, Barcelona, Espagne, 2020

Riemannian geometry and Cramér-Rao bound for blind separation of Gaussian sources

Bouchard, Breloy, Renaux, Ginolhac
2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2020), Barcelona, Espagne, 2020

Random Matrix Improved Covariance Estimation for a Large Class of Metrics

Tiomoko, Bouchard, Ginolhac, Couillet
ICML 2019 – 36th International Conference on Machine Learning, Long Beach, États-Unis, 2019

Dimensionality Reduction for BCI classification using Riemannian geometry

Coelho Rodrigues, Bouchard, Congedo, Jutten
BCI 2017 – 7th International Brain-Computer Interface Conference, Graz, Autriche, 2017

Réduction de dimension pour la Séparation Aveugle de Sources

Bouchard, Coelho Rodrigues, Malick, Congedo
GRETSI 2017 – XXVIème Colloque francophone de traitement du signal et des images, Juan-Les-Pins, France, 2017

Géométrie Riemannienne appliquée à la réduction de la dimension de signaux EEG pour les interfaces cerveau-machine

Coelho Rodrigues, Bouchard, Congedo, Jutten
GRETSI 2017 – XXVIème Colloque francophone de traitement du signal et des images, Juan-Les-Pins, France, 2017

Borne de Cramér-Rao intrinsèque pour la matrice de covariance des distributions elliptiques complexes

Breloy, Renaux, Ginolhac, Bouchard
GRETSI 2017 – XXVIème Colloque francophone de traitement du signal et des images, Juan-Les-Pins, France, 2017

A Closed-Form Unsupervised Geometry-Aware Dimensionality Reduction Method in the Riemannian Manifold of SPD Matrices

Congedo, Coelho Rodrigues, Bouchard, Barachant, Jutten
EMBC 2017 – 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju Island, Corée du Sud, 2017

Approximate Joint Diagonalization According to the Natural Riemannian Distance

Bouchard, Malick, Congedo
LVA/ICA 2017 – 13th International Conference on Latent Variable Analysis and Signal Separation, Grenoble, France, 2017

Approximate Joint Diagonalization within the Riemannian Geometry Framework

Bouchard, Korczowski, Malick, Congedo
EUSIPCO 2016 – 24th European Signal Processing Conference, Budapest, Hongrie, 2016