| Full text | |
| Author(s): |
Chaves, Gabriel S.
;
Lima, Markus V. S.
;
Yazdanpanah, Hamed
;
Diniz, Paulo S. R.
;
Ferreira, Tadeu N.
;
IEEE
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO); v. N/A, p. 5-pg., 2019-01-01. |
| Abstract | |
Many real systems have inherently some type of sparsity. Recently, the feature least-mean square (F-LMS) has been proposed to exploit hidden sparsity. Unlike the existing algorithms, the F-LMS algorithm performs a linear combination of the adaptive coefficients to reveal and then exploit the hidden sparsity. However, many systems have also plain besides hidden sparsity, and the F-LMS algorithm is not able to exploit the former. In this paper, we propose a new algorithm, named simple sparsity-aware F-LMS (SSF-LMS) algorithm, that is capable of exploiting both kinds of sparsity simultaneously. The hidden sparsity is exploited just like in the F-LMS algorithm, whereas the plain sparsity is exploited by means of the discard function applied to the filter coefficients. By doing so, the proposed SSF-LMS algorithm not only outperforms the F-LMS algorithm when plain sparsity is also observed, but also requires fewer arithmetic operations. Numerical results show that the proposed algorithm has faster speed of convergence and reaches lower steady-state mean-squared error (MSE) than the F-LMS and classical algorithms, when the system has plain and hidden sparsity. (AU) | |
| 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 |
| 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 |