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.
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Contributeur : Gaël Varoquaux <>
Soumis le : lundi 20 juin 2016 - 23:52:03
Dernière modification le : jeudi 7 février 2019 - 15:04:45


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


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



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