Publication: Iteratively Reweighted Least Squares Minimization with Nonzero Index Update
0
0
Issued Date
2021
Resource Type
Language
eng
File Type
application/pdf
Other identifier(s)
2-s2.0-85119430166
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
Proceedings - 1st International Conference on Smart Technologies Communication and Robotics, STCR 2021. Vol , No. (2021)
Suggested Citation
Tausiesakul B. Iteratively Reweighted Least Squares Minimization with Nonzero Index Update. Proceedings - 1st International Conference on Smart Technologies Communication and Robotics, STCR 2021. Vol , No. (2021). doi:10.1109/STCR51658.2021.9588830 Retrieved from: https://hdl.handle.net/20.500.14740/8156
Author(s)
Abstract
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.