Joint SVD-Hyperalignment for multi-subject FMRI data alignment

Inter-subject alignment is an important aspect of multi-subject fMRI research. Recently a method known as Hyperalignment has shown considerable success in attaining such alignment. In order to improve computational efficiency, we investigate a joint SVD-Hyperalignment algorithm. We show that this algorithm is more scalable than the standard Hyperalignment algorithm by providing analytic and empirical results using a multi-subject fMRI dataset. The experimental results show improved computation speed while maintaining between subject prediction accuracy on an image viewing experiment. In addition, our results provide benchmark relationships between voxel selection, accuracy and computation complexity for Hyperalignment, taking a joint SVD of the data, and joint SVD-Hyperalignment.