Constrained maximum likelihood based efficient dictionary learning for fMRI analysis

Abstract

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 analysis solely based on the sparsity of signals. However, the K-SVD algorithm is computationally demanding and may require many iterations to converge. Replacing SVD with an approximate solution for the d..