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Exploiting the convex-concave penalty for tracking: A novel dynamic reweighted sparse Bayesian learning algorithm


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Authors

Wang, Y 
Wipf, D 
Chen, W 
Wassell, IJ 

Abstract

We propose a novel dynamic reweighted ℓ2 (DRℓ2) algorithm in the regime of dynamic compressive sensing. Our analysis shows that aiming to solve a Type II optimization problem, DRℓ2 is effectively minimizing a `convex-concave' penalty in the coefficients that transitions from a convex region to a concave function using knowledge of past estimations. DRℓ2 thus provides superior reconstruction performance compared with state-of-the-art dynamic CS algorithms.

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Keywords

46 Information and Computing Sciences, 4006 Communications Engineering, 40 Engineering, 4603 Computer Vision and Multimedia Computation

Journal Title

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Conference Name

ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Journal ISSN

1520-6149

Volume Title

Publisher

IEEE
Sponsorship
Engineering and Physical Sciences Research Council (EP/K033700/1)