13 citations · 22 across the 17 of their papers we have counts for
18 papers
Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models
Jianhua Wang, Killian Cressant, Pedro Braconnot Velloso +1
This paper addresses graph learning in Gaussian Graphical Models (GGMs). In this context, data matrices often come with auxiliary metadata (e.g., textual descriptions associated wi…
Riemannian Change Point Detection on Manifolds with Robust Centroid Estimation
Xiuheng Wang, Ricardo Borsoi, Arnaud Breloy +1
Non-parametric change-point detection in streaming time series data is a long-standing challenge in signal processing. Recent advancements in statistics and machine learning have i…
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…
Sparse PCA with False Discovery Rate Controlled Variable Selection
Jasin Machkour, Arnaud Breloy, Michael Muma +2
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension. By imposing loading vectors to be sparse, it performs the…
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…