Social-sparsity brain decoders: faster spatial sparsity

Abstract : —Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. However, the state of the art, based on total variation or graph-net, is computationally costly. Here we introduce sparsity in the local neighborhood of each voxel with social-sparsity, a structured shrinkage operator. We find that, on brain imaging classification problems, social-sparsity performs almost as well as total-variation models and better than graph-net, for a fraction of the computational cost. It also very clearly outlines predictive regions. We give details of the model and the algorithm.
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01334551
Contributeur : Gaël Varoquaux <>
Soumis le : lundi 20 juin 2016 - 23:52:03
Dernière modification le : jeudi 7 février 2019 - 15:04:45

Fichiers

paper.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01334551, version 1
  • ARXIV : 1606.06439

Citation

Gaël Varoquaux, Matthieu Kowalski, Bertrand Thirion. Social-sparsity brain decoders: faster spatial sparsity. Pattern Recognition in NeuroImaging, Jun 2016, Trento, Italy. 2016, 〈http://prni2016.wix.com/prni2016〉. 〈hal-01334551〉

Partager

Métriques

Consultations de la notice

921

Téléchargements de fichiers

190