Showing stat.MLShow all
3 papers · 1 filter
stat.ML2024
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…
stat.ML2023
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…
stat.ML2023
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…