Adaptive complex-valued dictionary learning: Application to fMRI data analysis
Asif Iqbal, Mohamed Nait-Meziane, Abd-Krim Seghouane, Karim Abed-Meraim
Signal Processing | Elsevier | Published : 2020
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 ℓ1-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-..View full abstract
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Awarded by Australian Research Council
This work was supported in part by the Australian Research Council through Grant FT. 130101394 to Seghouane and lqbal.