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Adaptive complex-valued dictionary learning: Application to fMRI data analysis
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DOI：10.1016/j.sigpro.2019.107263.png)
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Abstract
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Complex-valued signals arise naturally in a wide-range of applications such as radar, magnetic resonance imaging (MRI), functional MRI (fMRI), remote sensing, communication systems, etc. In this article, we propose an adaptive dictionary learning (DL) algorithm for such complex-valued signals. The algorithm is derived via adaptively penalized, sequential rank-1 matrix approximations using the l(1)-norm as sparsity inducing penalty. Instead of alternating between sparse coding and dictionary update stages, each atom and its support are updated alternately with both variables admitting simple closed form solutions. A comprehensive performance comparison on simulated as well as experimental task-fMRI datasets is provided between the proposed DL method, complex-valued independent component analysis-entropy bound minimization (ICA-EBM), and magnitude-only ICA (Infomax) algorithms. The results highlight superior performance accuracy of the proposed algorithm w.r.t. the ICA-EBM and Infomax algorithms, in terms of true and false positive rates of the recovered task-related and default mode network (DMN) components. Our method was able to recover good quality phase maps as well, which can be used to further identify and suppress unwanted voxels. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Functional magnetic resonance imaging (fMRI)
Complex-valued data
Dictionary learning
Sparsity
Adaptive regularization
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