TY - GEN
T1 - The Pairwise Elastic Net support vector machine for automatic fMRI feature selection
AU - Lorbert, Alexander
AU - Ramadge, Peter Jeffrey
PY - 2013/10/18
Y1 - 2013/10/18
N2 - 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.
AB - 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.
KW - Feature Selection
KW - Pairwise Elastic Net
KW - Sparsity
KW - Support Vector Machine
KW - fMRI
UR - http://www.scopus.com/inward/record.url?scp=84890532757&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890532757&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6637807
DO - 10.1109/ICASSP.2013.6637807
M3 - Conference contribution
AN - SCOPUS:84890532757
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1036
EP - 1040
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -