4 citations · 10 across the 15 of their papers we have counts for
18 papers
A new approach for estimating the number of spikes in high-dimensional Gaussian models
Hugo Jeannin, Pascal Vallet, Florent Bouchard +2
In this paper, we study the estimation of the signal subspace dimension in a -variate signal-plus-noise complex Gaussian model, when the noise covariance matrix is assumed t…
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…
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…
A New Statistic for Testing Covariance Equality in High-Dimensional Gaussian Low-Rank Models
Rémi Beisson, Pascal Vallet, Audrey Giremus +1
In this paper, we consider the problem of testing equality of the covariance matrices of L complex Gaussian multivariate time series of dimension . We study the special case wh…
Covariance Fitting Interferometric Phase Linking: Modular Framework and Optimization Algorithms
Phan Viet Hoa Vu, Arnaud Breloy, Frédéric Brigui +2
Interferometric phase linking (IPL) has become a prominent technique for processing images of areas containing distributed scaterrers in SAR interferometry. Traditionally, IPL cons…
Online Change Detection in SAR Time-Series with Kronecker Product Structured Scaled Gaussian Models
Ammar Mian, Guillaume Ginolhac, Florent Bouchard +1
We develop the information geometry of scaled Gaussian distributions for which the covariance matrix exhibits a Kronecker product structure. This model and its geometry are then us…
Intrinsic Bayesian Cramér-Rao Bound with an Application to Covariance Matrix Estimation
Florent Bouchard, Alexandre Renaux, Guillaume Ginolhac +1
This paper presents a new performance bound for estimation problems where the parameter to estimate lies in a Riemannian manifold (a smooth manifold endowed with a Riemannian metri…
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…
Through the Wall Radar Imaging via Kronecker-structured Huber-type RPCA
Hugo Brehier, Arnaud Breloy, Chengfang Ren +1
The detection of multiple targets in an enclosed scene, from its outside, is a challenging topic of research addressed by Through-the-Wall Radar Imaging (TWRI). Traditionally, TWRI…
Robust Detection for Mills Cross Sonar
Olivier Lerda, Ammar Mian, Guillaume Ginolhac +2
Multi-array systems are widely used in sonar and radar applications. They can improve communication speeds, target discrimination, and imaging. In the case of a multibeam sonar sys…
Riemannian optimization for non-centered mixture of scaled Gaussian distributions
Antoine Collas, Arnaud Breloy, Chengfang Ren +2
This paper studies the statistical model of the non-centered mixture of scaled Gaussian distributions (NC-MSG). Using the Fisher-Rao information geometry associated to this distrib…
Robust Geometric Metric Learning
Antoine Collas, Arnaud Breloy, Guillaume Ginolhac +2
This paper proposes new algorithms for the metric learning problem. We start by noticing that several classical metric learning formulations from the literature can be viewed as mo…
Robust low-rank covariance matrix estimation with a general pattern of missing values
Alexandre Hippert-Ferrer, Mohammed Nabil El Korso, Arnaud Breloy +1
This paper tackles the problem of robust covariance matrix estimation when the data is incomplete. Classical statistical estimation methodologies are usually built upon the Gaussia…
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…
A Riemannian Framework for Low-Rank Structured Elliptical Models
Florent Bouchard, Arnaud Breloy, Guillaume Ginolhac +2
This paper proposes an original Riemmanian geometry for low-rank structured elliptical models, i.e., when samples are elliptically distributed with a covariance matrix that has a l…
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…
Convergence of Structured Quadratic Forms With Application to Theoretical Performances of Adaptive Filters in Low Rank Gaussian Context
Alice Combernoux, Frederic Pascal, Guillaume Ginolhac +1
This paper addresses the problem of deriving the asymptotic performance of adaptive Low Rank (LR) filters used in target detection embedded in a disturbance composed of a LR Gaussi…