A Simple Sparsity-aware Feature LMS Algorithm

Autor(es)

Chaves, Gabriel S. ; Lima, Markus V. S. ; Yazdanpanah, Hamed ; Diniz, Paulo S. R. ; Ferreira, Tadeu N. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO); v. N/A, p. 5-pg., 2019-01-01.

Resumo

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)

• 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