A single SVD sparse dictionary learning algorithm for FMRI data analysis

Data driven analysis methods such as independent component analysis (ICA) have proven to be well suited for analyzing functional magnetic resonance imaging (fMRI) data. Instead of using the independence assumption as in ICA approaches, we use the sparsity assumption to propose a novel overcomplete dictionary learning algorithm for statistical analysis of fMRI data. The proposed method differs from recent dictionary learning algorithms for sparse representation by updating all the dictionary atoms in parallel using only one SVD. Using both simulated and experimental fMRI data we show that the proposed method produces results comparable to those achieved with popular dictionary learning algorithms, but is more computationally efficient since the dictionary update is done using only one SVD.