The Pairwise Elastic Net support vector machine for automatic fMRI feature selection

A support vector machine (SVM) regularized with the Pairwise Elastic Net (PEN) penalty is used to automatically select a sparse set of brain voxel clusters based on the fMRI responses to two stimuli classes. This requires solving the PEN-SVM quadratic program. We show how to design the PEN regularization to encode, in a graph-based fashion, the pairwise similarity structure of the voxel fMRI responses and how to control the spatial locality of the encoding using a voxel searchlight. The voxel similarity encoding is reflected in the sparse structure of the weights of trained PEN-SVM and these weights automatically select a sparse set of voxel clusters. We empirically demonstrate the effectiveness of the approach using a real-world, multi-subject fMRI dataset.