Constrained maximum likelihood based efficient dictionary learning for fMRI analysis
A principal component analysis (PCA) based dictionary initialization approach accompanied by a computationally efficient dictionary learning algorithm for statistical analysis of functional magnetic resonance imaging (fMRI) is proposed. It replaces a singular value decomposition (SVD) computation with an approximate solution to obtain a local minima for a given initial dictionary. The K-SVD has been recently used to develop a data-driven sparse general linear model (GLM) framework for fMRI...[Show more]
|Collections||ANU Research Publications|
|Source:||2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014|
|01_Khalid_Constrained_maximum_likelihood_2014.pdf||1.48 MB||Adobe PDF||Request a copy|
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