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Generalized alternating projection for weighted-l2,1 minimization with applications to model-based compressive sensing

Publication ,  Journal Article
Liao, X; Li, H; Carin, L
Published in: SIAM Journal on Imaging Sciences
June 26, 2014

We consider the group basis pursuit problem, which extends basis pursuit by replacing the l1 norm with a weighted-L2,1 norm. We provide an anytime algorithm, called generalized alternating projection (GAP), to solve this problem. The GAP algorithm extends classical alternating projection to the case in which projections are performed between convex sets that undergo a systematic sequence of changes. We prove that, under a set of group-restricted isometry property (group-RIP) conditions, the reconstruction error of GAP monotonically converges to zero. Thus the algorithm can be interrupted at any time to return a valid solution and can be resumed subsequently to improve the solution. This anytime convergence property saves iterations on retracting and correcting mistakes, which, along with an effective acceleration scheme, makes GAP converge fast. Moreover, the periteration computation is inexpensive, consisting of sorting of a linear array followed by groupwise thresholding and linear transform of vectors for which fast algorithms often exist. We evaluate the algorithmic performance through extensive experiments in which GAP is compared to other state-of-the-art algorithms and applied to compressive sensing of natural images and video. © 2014 Society for Industrial and Applied Mathematics.

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Published In

SIAM Journal on Imaging Sciences

DOI

EISSN

1936-4954

Publication Date

June 26, 2014

Volume

7

Issue

2

Start / End Page

797 / 823

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4901 Applied mathematics
  • 4603 Computer vision and multimedia computation
 

Citation

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Liao, X., Li, H., & Carin, L. (2014). Generalized alternating projection for weighted-l2,1 minimization with applications to model-based compressive sensing. SIAM Journal on Imaging Sciences, 7(2), 797–823. https://doi.org/10.1137/130936658
Liao, X., H. Li, and L. Carin. “Generalized alternating projection for weighted-l2,1 minimization with applications to model-based compressive sensing.” SIAM Journal on Imaging Sciences 7, no. 2 (June 26, 2014): 797–823. https://doi.org/10.1137/130936658.
Liao X, Li H, Carin L. Generalized alternating projection for weighted-l2,1 minimization with applications to model-based compressive sensing. SIAM Journal on Imaging Sciences. 2014 Jun 26;7(2):797–823.
Liao, X., et al. “Generalized alternating projection for weighted-l2,1 minimization with applications to model-based compressive sensing.” SIAM Journal on Imaging Sciences, vol. 7, no. 2, June 2014, pp. 797–823. Scopus, doi:10.1137/130936658.
Liao X, Li H, Carin L. Generalized alternating projection for weighted-l2,1 minimization with applications to model-based compressive sensing. SIAM Journal on Imaging Sciences. 2014 Jun 26;7(2):797–823.

Published In

SIAM Journal on Imaging Sciences

DOI

EISSN

1936-4954

Publication Date

June 26, 2014

Volume

7

Issue

2

Start / End Page

797 / 823

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4901 Applied mathematics
  • 4603 Computer vision and multimedia computation