13 citations · 22 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Entropic Wasserstein Component Analysis
Antoine Collas, Titouan Vayer, Rémi Flamary +1
Dimension reduction (DR) methods provide systematic approaches for analyzing high-dimensional data. A key requirement for DR is to incorporate global dependencies among original an…
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…
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…