Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: —Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their ight maps are interpretable as they focus on a small number of regions. Hover, the state of the art, based on total variation or graph-net, is computationally costly. Here  introduce sparsity in the local neighborhood of each voxel with social-sparsity, a structured shrinkage operator.  find that, on brain imaging classification problems, social-sparsity performs almost as ll as total-variation models and better than graph-net, for a fraction of the computational cost. It also very clearly outlines predictive regions.  give details of the model and the algorithm.
['Gaël Varoquaux', 'M. Kowalski', 'Bertrand Thirion']
I am writing a research paper on a idea Social-sparsity brain decoders: faster spatial sparsity
[{"from": "human", "value": "I am writing a research paper on a idea Social-sparsity brain decoders: faster spatial sparsity"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: \u2014Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their ight maps are interpretable as they focus on a small number of regions. Hover, the state of the art, based on total variation or graph-net, is computationally costly. Here  introduce sparsity in the local neighborhood of each voxel with social-sparsity, a structured shrinkage operator.  find that, on brain imaging classification problems, social-sparsity performs almost as ll as total-variation models and better than graph-net, for a fraction of the computational cost. It also very clearly outlines predictive regions.  give details of the model and the algorithm."}]