Florent Bouchard
Florent Bouchard
LISTIC, University Savoie Mont-Blanc
Verified email at univ-smb.fr - Homepage
Title
Cited by
Cited by
Year
Dimensionality Reduction for BCI classification using Riemannian geometry
P Rodrigues, F Bouchard, M Congedo, C Jutten
112017
Intrinsic cramér–rao bounds for scatter and shape matrices estimation in ces distributions
A Breloy, G Ginolhac, A Renaux, F Bouchard
IEEE Signal Processing Letters 26 (2), 262-266, 2018
92018
Riemannian optimization and approximate joint diagonalization for blind source separation
F Bouchard, J Malick, M Congedo
IEEE Transactions on Signal Processing 66 (8), 2041-2054, 2018
82018
A closed-form unsupervised geometry-aware dimensionality reduction method in the Riemannian Manifold of SPD matrices
M Congedo, PLC Rodrigues, F Bouchard, A Barachant, C Jutten
2017 39th Annual International Conference of the IEEE Engineering in …, 2017
62017
Approximate joint diagonalization within the Riemannian geometry framework
F Bouchard, L Korczowski, J Malick, M Congedo
2016 24th European Signal Processing Conference (EUSIPCO), 210-214, 2016
62016
Approximate joint diagonalization according to the natural Riemannian distance
F Bouchard, J Malick, M Congedo
International Conference on Latent Variable Analysis and Signal Separation …, 2017
52017
Mining the bilinear structure of data with approximate joint diagonalization
L Korczowski, F Bouchard, C Jutten, M Congedo
2016 24th European Signal Processing Conference (EUSIPCO), 667-671, 2016
52016
Random matrix improved covariance estimation for a large class of metrics
M Tiomoko, F Bouchard, G Ginholac, R Couillet
arXiv preprint arXiv:1902.02554, 2019
42019
Approximate joint diagonalization with Riemannian optimization on the general linear group
F Bouchard, B Afsari, J Malick, M Congedo
SIAM Journal on Matrix Analysis and Applications 41 (1), 152-170, 2020
32020
Géométrie Riemannienne appliquée à la réduction de la dimension de signaux EEG pour les interfaces cerveau-machine
P Rodrigues, F Bouchard, M Congedo, C Jutten
12017
Réduction de dimension pour la Séparation Aveugle de Sources
F Bouchard, P Rodrigues, J Malick, M Congedo
12017
Borne de Cramér-Rao intrinsèque pour la matrice de covariance des distributions elliptiques complexes
A Breloy, A Renaux, G Ginolhac, F Bouchard
12017
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection
F Bouchard, A Mian, J Zhou, S Said, G Ginolhac, Y Berthoumieu
arXiv preprint arXiv:2005.10087, 2020
2020
Riemannian Framework for Robust Covariance Matrix Estimation in Spiked Models
F Bouchard, A Breloy, G Ginolhac, F Pascal
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
2020
Riemannian Geometry and Cramér-rao Bound for Blind Separation of Gaussian Sources
F Bouchard, A Breloy, A Renaux, G Ginolhac
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
2020
A Riemannian Framework for Low-Rank Structured Elliptical Models
F Bouchard, A Breloy, G Ginolhac, A Renaux, F Pascal
arXiv preprint arXiv:2001.01141, 2020
2020
Bornes de Cramér-Rao Intrinseques pour l’estimation de la matrice de dispersion normalisée dans les distributions elliptiques
F Bouchard, A Breloy, A Renaux, G Ginolhac
GRETSI 2019, 2019
2019
Intrinsic Cramér-Rao Bounds for Scatter and Shape Matrices Estimation in Complex Elliptically Symmetric Distributions
A Breloy, G Ginolhac, A Renaux, F Bouchard
2018
Géométrie et optimisation riemannienne pour la diagonalisation conjointe: application à la séparation de sources d'électroencéphalogrammes
F Bouchard
2018
Extracting EEG sources of ERP based BCI by Composite Approximate Joint Diagonalization
L Korczowski, F Bouchard, C Jutten, M Congedo
2018
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