Iteratively Reweighted Least Squares by Diagonal Regularization
12
Issued Date
2023-01-01
Resource Type
Scopus ID
2-s2.0-85169297879
Journal Title
Proceedings of JCSSE 2023 - 20th International Joint Conference on Computer Science and Software Engineering
Start Page
112
End Page
117
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings of JCSSE 2023 - 20th International Joint Conference on Computer Science and Software Engineering (2023) , 112-117
Suggested Citation
Tausiesakul B., Asavaskulkiet K. Iteratively Reweighted Least Squares by Diagonal Regularization. Proceedings of JCSSE 2023 - 20th International Joint Conference on Computer Science and Software Engineering (2023) , 112-117. 117. doi:10.1109/JCSSE58229.2023.10202058 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/89594
Title
Iteratively Reweighted Least Squares by Diagonal Regularization
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
We consider a sparse signal reconstruction problem. The signal can be captured into a vector whose elements can be zeros. Standing for iteratively reweighted least squares, IRLS is a technique designed for extracting the signal vector from the available observation data. A new algorithm based on the IRLS is proposed by using diagonal regularization for sparse image reconstruction. A closed-form solution of the IRLS minimization is derived and then we have developed a variational IRLS algorithm based on the available solution. Since the matrix inverse in the iterative computation can be subject to ill condition, we apply a diagonal regularization to such a problem. Numerical simulation is conducted to illustrate the performance of the new IRLS with the comparison to the former IRLS algorithm. Numerical results indicate that the new IRLS method provides lower signal recovery error than the conventional IRLS approach at the expense of more complexity in terms of more computational time.