Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81440
Title: Greedy pursuits based gradual weighting strategy for weighted ℓ1-minimization
Authors: Narayanan, Sathiya
Sahoo, Sujit Kumar
Makur, Anamitra
Keywords: Weighted ℓ1-minimization
Greedy Pursuits Assisted Basis Pursuit
Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Narayanan, S., Sahoo, S. K., & Makur, A. (2018). Greedy pursuits based gradual weighting strategy for weightedℓ1-minimization. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/ICASSP.2018.8462645
Abstract: In Compressive Sensing (CS) of sparse signals, standard ℓ 1 -minimization can be effectively replaced with Weighted ℓ 1 -minimization (Wℓ 1 ) if some information about the signal or its sparsity pattern is available. If no such information is available, Re-Weighted ℓ 1 -minimization (ReWℓ 1 ) can be deployed. ReW ℓ 1 solves a series of Wℓ 1 problems, and therefore, its computational complexity is high. An alternative to ReWℓ 1 is the Greedy Pursuits Assisted Basis Pursuit (GPABP) which employs multiple Greedy Pursuits (GPs) to obtain signal information which in turn is used to run Wℓ 1 . Although GPABP is an effective fusion technique, it adapts a binary weighting strategy for running Wℓ 1 , which is very restrictive. In this article, we propose a gradual weighting strategy for Wℓ 1 , which handles the signal estimates resulting from multiple GPs more effectively compared to the binary weighting strategy of GPABP. The resulting algorithm is termed as Greedy Pursuits assisted Weighted ℓ 1 -minimization (GP-Wℓ 1 ). For GP-Wℓ 1 , we derive the theoretical upper bound on its reconstruction error. Through simulation results, we show that the proposed GP-Wℓ 1 outperforms ReWℓ 1 and the state-of-the-art GPABP.
URI: https://hdl.handle.net/10356/81440
http://hdl.handle.net/10220/50384
DOI: 10.1109/ICASSP.2018.8462645
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICASSP.2018.8462645
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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