Performance evaluation of matrix factorization for fMRI data
Yusuke Endo Koujin Takeda
In the study of the brain, there is a hypothesis that sparse coding is
realized in information representation of external stimuli, which is
experimentally confirmed for visual stimulus recently. However, unlike the
specific functional region in the brain, sparse coding in information
processing in the whole brain has not been clarified sufficiently. In this
study, we investigate the validity of sparse coding in the whole human brain by
applying various matrix factorization methods to functional magnetic resonance
imaging data of neural activities in the whole human brain. The result suggests
sparse coding hypothesis in information representation in the whole human
brain, because extracted features from sparse MF method, SparsePCA or MOD under
high sparsity setting, or approximate sparse MF method, FastICA, can classify
external visual stimuli more accurately than non-sparse MF method or sparse MF
method under low sparsity setting.