Reweighted nonnegative least-mean-square algorithm
Statistical inference subject to nonnegativity constraints is a frequently occurring problem in learning problems. The nonnegative least-mean-square (NNLMS) algorithm was derived to address such problems in an online way. This algorithm builds on a fixed-point iteration strategy driven by the Karush...
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| Published in | Signal processing Vol. 128; pp. 131 - 141 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.11.2016
Elsevier |
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| ISSN | 0165-1684 1872-7557 |
| DOI | 10.1016/j.sigpro.2016.03.017 |
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| Abstract | Statistical inference subject to nonnegativity constraints is a frequently occurring problem in learning problems. The nonnegative least-mean-square (NNLMS) algorithm was derived to address such problems in an online way. This algorithm builds on a fixed-point iteration strategy driven by the Karush–Kuhn–Tucker conditions. It was shown to provide low variance estimates, but it however suffers from unbalanced convergence rates of these estimates. In this paper, we address this problem by introducing a variant of the NNLMS algorithm. We provide a theoretical analysis of its behavior in terms of transient learning curve, steady-state and tracking performance. We also introduce an extension of the algorithm for online sparse system identification. Monte-Carlo simulations are conducted to illustrate the performance of the algorithm and to validate the theoretical results.
•We proposed a variant of NN-LMS algorithm with balanced weight convergence rates.•Accurate performance analysis is performed for a general nonstationarity model.•The sparse system identification problem can be solved via the derived algorithm. |
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| AbstractList | Statistical inference subject to nonnegativity constraints is a frequently occurring problem in learning problems. The nonnegative least-mean-square (NNLMS) algorithm was derived to address such problems in an online way. This algorithm builds on a fixed-point iteration strategy driven by the Karush-Kuhn-Tucker conditions. It was shown to provide low variance estimates, but it however suffers from unbalanced convergence rates of these estimates. In this paper, we address this problem by introducing a variant of the NNLMS algorithm. We provide a theoretical analysis of its behavior in terms of transient learning curve, steady-state and tracking performance. We also introduce an extension of the algorithm for online sparse system identification. Monte-Carlo simulations are conducted to illustrate the performance of the algorithm and to validate the theoretical results. Statistical inference subject to nonnegativity constraints is a frequently occurring problem in learning problems. The nonnegative least-mean-square (NNLMS) algorithm was derived to address such problems in an online way. This algorithm builds on a fixed-point iteration strategy driven by the Karush–Kuhn–Tucker conditions. It was shown to provide low variance estimates, but it however suffers from unbalanced convergence rates of these estimates. In this paper, we address this problem by introducing a variant of the NNLMS algorithm. We provide a theoretical analysis of its behavior in terms of transient learning curve, steady-state and tracking performance. We also introduce an extension of the algorithm for online sparse system identification. Monte-Carlo simulations are conducted to illustrate the performance of the algorithm and to validate the theoretical results. •We proposed a variant of NN-LMS algorithm with balanced weight convergence rates.•Accurate performance analysis is performed for a general nonstationarity model.•The sparse system identification problem can be solved via the derived algorithm. |
| Author | Richard, Cédric Chen, Jie Bermudez, José Carlos M. |
| Author_xml | – sequence: 1 givenname: Jie surname: Chen fullname: Chen, Jie email: dr.jie.chen@ieee.org organization: Center of Intelligent Acoustics and Immersive Communications (CIAIC), School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China – sequence: 2 givenname: Cédric surname: Richard fullname: Richard, Cédric email: cedric.richard@unice.fr organization: The Université de Nice Sophia-Antipolis, UMR CNRS 7293, Observatoire de la Côte d'azur, Laboratoire Lagrange, Parc Valrose, 06102 Nice, France – sequence: 3 givenname: José Carlos M. surname: Bermudez fullname: Bermudez, José Carlos M. email: j.bermudez@ieee.org organization: The Department of Electrical Engineering, Federal University of Santa Catarina, 88040-900 Florianópolis, SC, Brazil |
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| Keywords | Behavior analysis Sparse system identification Online system identification Nonnegativity constraints |
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| SubjectTerms | Algorithms Behavior analysis Computer Science Computer simulation Construction Convergence Estimates Nonnegativity constraints On-line systems Online system identification Signal and Image Processing Sparse system identification Strategy Tracking |
| Title | Reweighted nonnegative least-mean-square algorithm |
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