Communication Dans Un Congrès Année : 2024

Leveraging Standardization in Graph Learning

Résumé

Standardization of the variables is a staple scaling tool that can be beneficial in most statistical learning tasks. In the context of graph learning, we propose to take further advantage of this pre-processing, as it yields an additional implicit normalization structure for the covariance matrix. For Gaussian graphical models, we propose a new algorithm that aims at estimating a sparse precision matrix while its inverse is constrained to be a (possibly low-rank structured) correlation matrix. The corresponding optimization problem is addressed by using the framework of Riemannian optimization. Simulations over various graph structures illustrate the interest of the proposed approach.
Fichier principal
Vignette du fichier
Standardized_GLasso___Eusipco_2024.pdf (408) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04952046 , version 1 (17-02-2025)

Licence

Identifiants

  • HAL Id : hal-04952046 , version 1

Citer

Thu Ha Phi, Alexandre Hippert-Ferrer, Florent Bouchard, Arnaud Breloy. Leveraging Standardization in Graph Learning. 32nd European Signal Processing Conference (EUSIPCO), Aug 2024, Lyon, France. ⟨hal-04952046⟩
0 Consultations
0 Téléchargements

Partager

More