| Full text | |
| Author(s): |
Yazdanpanah, Hamed
[1]
;
Apolinario Jr, Jose A.
Total Authors: 2
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Stat, Dept Comp Sci, BR-05508090 Sao Paulo, SP - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | CIRCUITS SYSTEMS AND SIGNAL PROCESSING; v. 40, n. 1 JUN 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
The feature least-mean-square (F-LMS) algorithm has already been introduced to exploit hidden sparsity in lowpass and highpass systems. In this paper, by proposing the extended F-LMS (EF-LMS) algorithm, we boosted the F-LMS algorithm to exploit hidden sparsity in more general systems, thosewhich are neither lowpass nor highpass. To this end, by means of the so-called feature matrix, we reveal the hidden sparsity in coefficients and utilize the l(1)-norm to exploit the exposed sparsity. As a result, the EF-LMS algorithm will improve the convergence rate and the steady-state mean-squared error (MSE) as compared to the traditional least-mean-square algorithm. Moreover, in thiswork, we analyze the convergence behavior of the coefficient vector and the steady-state MSE performance of the EF-LMS algorithm. Through synthetic and real-world experiments, it has been seen that the EF-LMS algorithm can improve the convergence rate and the steady-state MSE whenever the hidden sparsity is revealed. (AU) | |
| FAPESP's process: | 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery |
| Grantee: | Roberto Marcondes Cesar Junior |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 19/06280-1 - Integration, transformation, dataset augmentation and quality control for intermediate representation |
| Grantee: | Hamed Yazdanpanah |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |