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Adaptive algorithm ...
Adaptive algorithm for sparse signal recovery
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- Bayisa, Fekadu (författare)
- Umeå universitet,Institutionen för matematik och matematisk statistik,Umeå University
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- Zhou, Zhiyong, 1989- (författare)
- Umeå universitet,Institutionen för matematik och matematisk statistik,Umeå University
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- Cronie, Ottmar (författare)
- Umeå universitet,Institutionen för matematik och matematisk statistik,Mathematical Statistics,Umeå University
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- Yu, Jun, 1962- (författare)
- Umeå universitet,Institutionen för matematik och matematisk statistik,Mathematical Statistics,Umeå University
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(creator_code:org_t)
- Elsevier, 2019
- 2019
- Engelska.
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Ingår i: Digital signal processing (Print). - : Elsevier. - 1051-2004 .- 1095-4333. ; 87, s. 10-18
- Relaterad länk:
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http://arxiv.org/pdf...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- The development of compressive sensing in recent years has given much attention to sparse signal recovery. In sparse signal recovery, spike and slab priors are playing a key role in inducing sparsity. The use of such priors, however, results in non-convex and mixed integer programming problems. Most of the existing algorithms to solve non-convex and mixed integer programming problems involve either simplifying assumptions, relaxations or high computational expenses. In this paper, we propose a new adaptive alternating direction method of multipliers (AADMM) algorithm to directly solve the suggested non-convex and mixed integer programming problem. The algorithm is based on the one-to-one mapping property of the support and non-zero element of the signal. At each step of the algorithm, we update the support by either adding an index to it or removing an index from it and use the alternating direction method of multipliers to recover the signal corresponding to the updated support. Moreover, as opposed to the competing “adaptive sparsity matching pursuit” and “alternating direction method of multipliers” methods our algorithm can solve non-convex problems directly. Experiments on synthetic data and real-world images demonstrated that the proposed AADMM algorithm provides superior performance and is computationally cheaper than the recently developed iterative convex refinement (ICR) and adaptive matching pursuit (AMP) algorithms.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- sparsity
- adaptive algorithm
- sparse signal recovery
- spike and slab priors
- matematisk statistik
- Mathematical Statistics
- Signal Processing
- signalbehandling
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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