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We propose a novel computationally efficient hierarchical dictionary learning (HDL) approach for data-driven unmixing and functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. It is shown that by simultaneously exploiting the sparsity of the spatial brain maps and the incoherence among their evolution in time or task functions, one can achieve better performance while...
It has been shown recently that incorporating priori knowledge into the basic compressive sensing results in significant improvement of its performance. This has motivated us to extend the incorporation of partial known support into the problem of Robust Principal Component Analysis (RPCA) from compressive measurements. Our proposed algorithm utilizes the known part of the support to recover a matrix...
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