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 overcom-plete 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...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Workshop on Statistical Signal Processing Proceedings|
|01_Khalid_A_single_SVD_sparse_dictionary_2014.pdf||1.06 MB||Adobe PDF||Request a copy|
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