Publications

Online change detection in SAR time-series with Kronecker product structured scaled Gaussian models

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

A Riemannian Framework for Low-Rank Structured Elliptical Models

On Riemannian and non-Riemannian Optimisation, and Optimisation Geometry

Random matrix improved covariance estimation for a large class of metrics

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

Approximate joint diagonalization with Riemannian optimization on the general linear group

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

Riemannian Optimization and Approximate Joint Diagonalization for Blind Source Separation

Random matrix theory improved Fréchet mean of symmetric positive definite matrices

Robust Low-Rank Correlation Fitting

Learning Graphical Factor Models with Riemannian Optimization

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

Distribution matricielle t-Wishart : géométrie d’information, estimation et application pour la classification de signaux EEG

Estimation de barycentres sur variétés de Stiefel : une approche par projection

Optimisation Riemannienne pour l’apprentissage de graphes structurés

Elliptical Wishart Distribution: Maximum Likelihood Estimator from Information Geometry

Riemannian classification of EEG signals with missing values

ON THE USE OF GEODESIC TRIANGLES BETWEEN GAUSSIAN DISTRIBUTIONS FOR CLASSIFICATION PROBLEMS

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

A Tyler-type estimator of location and scatter leveraging Riemannian optimization

A Riemannian approach to blind separation of t-distributed sources

Riemannian framework for robust covariance matrix estimation in spiked models

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

Bornes de Cramér-Rao Intrinsèques pour l’estimation de la matrice de dispersion normalisée dans les distributions elliptiques

Random Matrix Improved Covariance Estimation for a Large Class of Metrics

Dimensionality Reduction for BCI classification using Riemannian geometry

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

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

Réduction de dimension pour la Séparation Aveugle de Sources

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

Approximate Joint Diagonalization According to the Natural Riemannian Distance

Mining the Bilinear Structure of Data with Approximate Joint Diagonalization

Approximate Joint Diagonalization within the Riemannian Geometry Framework