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Conference Papers Year : 2016

Social-sparsity brain decoders: faster spatial sparsity


—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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hal-01334551 , version 1 (20-06-2016)



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