Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Structured regularization for conditional Gaussian graphical models

Abstract : Conditional Gaussian graphical models are a reparametrization of the multivariate linear regression model which explicitly exhibits (i) the partial covariances between the predictors and the responses, and (ii) the partial covariances between the responses themselves. Such models are particularly suitable for interpretability since partial covariances describe direct relationships between variables. In this framework, we propose a regularization scheme to enhance the learning strategy of the model by driving the selection of the relevant input features by prior structural information. It comes with an efficient alternating optimization procedure which is guaranteed to converge to the global minimum. On top of showing competitive performance on artificial and real datasets, our method demonstrates capabilities for fine interpretation, as illustrated on three high-dimensional datasets from spectroscopy, genetics, and genomics.
Type de document :
Article dans une revue
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01531660
Déposant : Archive Ouverte Prodinra <>
Soumis le : jeudi 1 juin 2017 - 20:05:20
Dernière modification le : lundi 4 janvier 2021 - 11:30:05

Lien texte intégral

Identifiants

Collections

Citation

Julien Chiquet, Tristan Mary-Huard, Stephane Robin. Structured regularization for conditional Gaussian graphical models. Statistics and Computing, Springer Verlag (Germany), 2016, 27 (3), pp.1-16. ⟨10.1007/s11222-016-9654-1⟩. ⟨hal-01531660⟩

Partager

Métriques

Consultations de la notice

214