| Full text | |
| Author(s): |
Yazdanpanah, Hamed
;
Apolinario, Jose A., Jr.
;
Matthews, MB
Total Authors: 3
|
| Document type: | Journal article |
| Source: | CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS; v. N/A, p. 4-pg., 2019-01-01. |
| Abstract | |
Recently, a great effort has been observed to exploit sparsity in physical parameters; however, in many cases, sparsity is hidden in relations between parameters, and some appropriate tools should be utilized to expose it. In this paper, a family of algorithms called feature normalized least-mean-square (F-NLMS) algorithms is proposed to exploit hidden sparsity. The major key of these algorithms is a so-called feature matrix that transforms non-sparse systems to new systems containing sparsity; then the revealed sparsity is exploited by some sparsity-promoting penalty function. Numerical results demonstrate that the F-NLMS algorithms can reduce the steady-state mean-squared-error (MSE) and/or improve the convergence rate of the learning process significantly. (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 |