Compressed Online Dictionary Learning for Fast fMRI Decomposition
  We present a method for fast resting-state fMRI spatial decomposi-tions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects. Introducing a measure of correspondence between spatial decompositions of rest fMRI, we demonstrates that time-reduced dictionary learning produces result as reliable as non-reduced decompositions. We also show that this reduction significantly improves computational scalability. 
Arthur Mensch (PARIETAL), Ga\"el Varoquaux (PARIETAL), Bertrand   Thirion (PARIETAL)
stat.ML cs.LG