Florent Bouchard

According to our database, Florent Bouchard authored at least 25 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Natural Bayesian Cramér-Rao Bound with an Application to Covariance Estimation.

Florent Bouchard, Alexandre Renaux, Guillaume Ginolhac, Arnaud Breloy

CoRR, 2023

The Fisher-Rao geometry of CES distributions.

Florent Bouchard, Arnaud Breloy, Antoine Collas, Alexandre Renaux, Guillaume Ginolhac

CoRR, 2023

Learning Graphical Factor Models with Riemannian Optimization.

Alexandre Hippert-Ferrer, Florent Bouchard, Ammar Mian, Titouan Vayer, Arnaud Breloy

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Elliptical Wishart Distribution: Maximum Likelihood Estimator from Information Geometry.

Imen Ayadi, Florent Bouchard, Frédéric Pascal

Proceedings of the IEEE International Conference on Acoustics, 2023

t-WDA: A novel Discriminant Analysis applied to EEG classification.

Imen Ayadi, Florent Bouchard, Frédéric Pascal

Proceedings of the 31st European Signal Processing Conference, 2023

2022

On the Use of Geodesic Triangles between Gaussian Distributions for Classification Problems.

Antoine Collas, Florent Bouchard, Guillaume Ginolhac, Arnaud Breloy, Chengfang Ren, Jean Philippe Ovarlez

Proceedings of the IEEE International Conference on Acoustics, 2022

Riemannian Classification of EEG Signals with Missing Values.

Alexandre Hippert-Ferrer, Ammar Mian, Florent Bouchard, Frédéric Pascal

Proceedings of the 30th European Signal Processing Conference, 2022

2021

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

Antoine Collas, Florent Bouchard, Arnaud Breloy, Guillaume Ginolhac, Chengfang Ren, Jean Philippe Ovarlez

IEEE Trans. Signal Process., 2021

A Riemannian Framework for Low-Rank Structured Elliptical Models.

Florent Bouchard, Arnaud Breloy, Guillaume Ginolhac, Alexandre Renaux, Frédéric Pascal

IEEE Trans. Signal Process., 2021

A Tyler-Type Estimator of Location and Scatter Leveraging Riemannian Optimization.

Antoine Collas, Florent Bouchard, Arnaud Breloy, Chenfang Ren, Guillaume Ginolhac, Jean Philippe Ovarlez

Proceedings of the IEEE International Conference on Acoustics, 2021

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

Florent Bouchard, Arnaud Breloy, Ammar Mian, Guillaume Ginolhac

Proceedings of the 29th European Signal Processing Conference, 2021

2020

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

Florent Bouchard, Ammar Mian, Jialun Zhou, Salem Said, Guillaume Ginolhac, Yannick Berthoumieu

Signal Process., 2020

Approximate Joint Diagonalization with Riemannian Optimization on the General Linear Group.

Florent Bouchard, Bijan Afsari, Jérôme Malick, Marco Congedo

SIAM J. Matrix Anal. Appl., 2020

Riemannian Geometry and Cramér-rao Bound for Blind Separation of Gaussian Sources.

Florent Bouchard, Arnaud Breloy, Alexandre Renaux, Guillaume Ginolhac

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Riemannian Framework for Robust Covariance Matrix Estimation in Spiked Models.

Florent Bouchard, Arnaud Breloy, Guillaume Ginolhac, Frédéric Pascal

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

A Riemannian approach to blind separation of t-distributed sources.

Florent Bouchard, Arnaud Breloy, Guillaume Ginolhac, Alexandre Renaux

Proceedings of the 28th European Signal Processing Conference, 2020

2019

Intrinsic Cramér-Rao Bounds for Scatter and Shape Matrices Estimation in CES Distributions.

Arnaud Breloy, Guillaume Ginolhac, Alexandre Renaux, Florent Bouchard

IEEE Signal Process. Lett., 2019

Random Matrix Improved Covariance Estimation for a Large Class of Metrics.

Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Géométrie et optimisation riemannienne pour la diagonalisation conjointe : application à la séparation de sources d'électroencéphalogrammes. (Riemannian geometry and optimization for approximate joint diagonalization : application to source separation of electroencephalograms).

Florent Bouchard

PhD thesis, 2018

Riemannian Optimization and Approximate Joint Diagonalization for Blind Source Separation.

Florent Bouchard, Jérôme Malick, Marco Congedo

IEEE Trans. Signal Process., 2018

2017

Approximate Joint Diagonalization According to the Natural Riemannian Distance.

Florent Bouchard, Jérôme Malick, Marco Congedo

Proceedings of the Latent Variable Analysis and Signal Separation, 2017

Dimensionality Reduction for BCI Classification using Riemannian Geometry.

Pedro Luiz Coelho Rodrigues, Florent Bouchard, Marco Congedo, Christian Jutten

Proceedings of the From Vision to Reality, 2017

A closed-form unsupervised geometry-aware dimensionality reduction method in the Riemannian Manifold of SPD matrices.

Marco Congedo, Pedro Luiz Coelho Rodrigues, Florent Bouchard, Alexandre Barachant, Christian Jutten

Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017

2016

Mining the bilinear structure of data with approximate joint diagonalization.

Louis Korczowski, Florent Bouchard, Christian Jutten, Marco Congedo

Proceedings of the 24th European Signal Processing Conference, 2016

Approximate joint diagonalization within the Riemannian geometry framework.

Florent Bouchard, Louis Korczowski, Jérôme Malick, Marco Congedo

Proceedings of the 24th European Signal Processing Conference, 2016