Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/17565
ชื่อเรื่อง: Iteratively Reweighted Least Squares Minimization with Nonzero Index Update
ผู้แต่ง: Tausiesakul B.
Keywords: Iterative methods
Mean square error
Compressive sensing
Discrete-time signals
Index update
Iteratively reweighted least square minimization
Iteratively reweighted least-squares
Least squares minimization
Lp-norm
Optimisations
Sensing problems
Sparsity support
Compressed sensing
วันที่เผยแพร่: 2021
บทคัดย่อ: The acquisition of a discrete-time signal is an important part in compressive sensing problem. Instead of using l0-norm optimization, much attention is paid to lp-norm formulation for p ? (0,1) due to its fast convergence and comparable accuracy. Iteratively reweighted least squares (IRLS) minimization is known as an improved algorithm of the typical basis pursuit with l1-norm criterion. In this work, an alternative enhancement of the IRLS criterion is presented. The proposed method invokes a descending sort of the absolute values of all elements in the solution and updates the nonzero indices in each iteration. Numerical examples illustrate that the proposed nonzero index update can help the IRLS minimization to recover the sparse signal with lower normalized root mean square error. © 2021 IEEE.
URI: https://ir.swu.ac.th/jspui/handle/123456789/17565
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119430166&doi=10.1109%2fSTCR51658.2021.9588830&partnerID=40&md5=9b0215e06fe35277b393b5b5192f9997
Appears in Collections:Scopus 1983-2021

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