12 papers
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…
SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
Bruno Aristimunha, Ce Ju, Antoine Collas +5
Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages. Existing impl…
Generalized Nonnegative Structured Kruskal Tensor Regression
Xinjue Wang, Esa Ollila, Sergiy A. Vorobyov +1
This paper introduces Generalized Nonnegative Structured Kruskal Tensor Regression (NS-KTR), a novel tensor regression framework that enhances interpretability and performance thro…
Classification of Buried Objects from Ground Penetrating Radar Images by using Second Order Deep Learning Models
Douba Jafuno, Ammar Mian, Guillaume Ginolhac +1
In this paper, a new classification model based on covariance matrices is built in order to classify buried objects. The inputs of the proposed models are the hyperbola thumbnails…
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…
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…
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…
Learning Graphical Factor Models with Riemannian Optimization
Alexandre Hippert-Ferrer, Florent Bouchard, Ammar Mian +2
Graphical models and factor analysis are well-established tools in multivariate statistics. While these models can be both linked to structures exhibited by covariance and precisio…
Riemannian classification of EEG signals with missing values
Alexandre Hippert-Ferrer, Ammar Mian, Florent Bouchard +1
This paper proposes a strategy to handle missing data for the classification of electroencephalograms using covariance matrices. It relies on the observed-data likelihood within an…
Block-wise Minimization-Majorization algorithm for Huber's criterion: sparse learning and applications
Esa Ollila, Ammar Mian
Huber's criterion can be used for robust joint estimation of regression and scale parameters in the linear model. Huber's (Huber, 1981) motivation for introducing the criterion ste…
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…