Basis Expansions Approaches for Regularized Sequential Dictionary Learning Algorithms with Enforced Sparsity for fMRI Data Analysis.

IEEE Trans Med Imaging. 2017 Apr 28;:

Authors: Seghouane AK, Iqbal A

Abstract
Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of temporal smoothness in the column direction. This prior information which can be converted to a constraint of smoothness on the learned dictionary atoms has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differs from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI datasets illustrating the performance of the proposed algorithms are provided.

PMID: 28463189 [PubMed - as supplied by publisher]