The Extended Feature LMS Algorithm: Exploiting Hidden Sparsity for Systems with Unknown Spectrum
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, those which ar...
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| Published in | Circuits, systems, and signal processing Vol. 40; no. 1; pp. 174 - 192 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.01.2021
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 0278-081X 1531-5878 |
| DOI | 10.1007/s00034-020-01461-3 |
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| 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, those which 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 this work, 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. |
|---|---|
| AbstractList | 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, those which 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 l1-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 this work, 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. 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, those which 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 this work, 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. |
| Author | Apolinário, José A. Yazdanpanah, Hamed |
| Author_xml | – sequence: 1 givenname: Hamed orcidid: 0000-0002-7108-7866 surname: Yazdanpanah fullname: Yazdanpanah, Hamed email: hamed.yazdanpanah@smt.ufrj.br organization: Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo – sequence: 2 givenname: José A. orcidid: 0000-0003-1426-9636 surname: Apolinário fullname: Apolinário, José A. organization: Programs of Defense and Electrical Engineering, Military Institute of Engineering (IME) |
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| Snippet | 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... |
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| SubjectTerms | Algorithms Circuits and Systems Convergence Electrical Engineering Electronics and Microelectronics Engineering Instrumentation Signal,Image and Speech Processing Sparsity Steady state |
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| Title | The Extended Feature LMS Algorithm: Exploiting Hidden Sparsity for Systems with Unknown Spectrum |
| URI | https://link.springer.com/article/10.1007/s00034-020-01461-3 https://www.proquest.com/docview/2478378289 |
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