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Sparse Signal Recovery Using Iterative Proximal Projection
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
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2018 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 4, p. 879-894Article in journal (Refereed) Published
Abstract [en]

This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify that the proposed algorithm considerably outperforms some well-known and recently proposed algorithms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 66, no 4, p. 879-894
Keywords [en]
Sparse signal recovery, compressed sensing, SL0, proximal splitting algorithms, iterative sparsification-projection
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-223260DOI: 10.1109/TSP.2017.2778695ISI: 000423703600003Scopus ID: 2-s2.0-85037644363OAI: oai:DiVA.org:kth-223260DiVA, id: diva2:1183374
Note

QC 20180216

Available from: 2018-02-16 Created: 2018-02-16 Last updated: 2018-02-16Bibliographically approved

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Chatterjee, Saikat

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