| Texto completo | |
| Autor(es): |
Yazdanpanah, Hamed
;
Apolinario, Jose A., Jr.
;
Matthews, MB
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS; v. N/A, p. 4-pg., 2019-01-01. |
| Resumo | |
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) | |
| Processo FAPESP: | 19/06280-1 - Integração, transformação, aumento de dados e controle de qualidade para representações intermediárias |
| Beneficiário: | Hamed Yazdanpanah |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 15/22308-2 - Representações intermediárias em Ciência Computacional para descoberta de conhecimento |
| Beneficiário: | Roberto Marcondes Cesar Junior |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |