Acceleration of adaptive proximal forward-backward splitting method and its application to sparse system identification

In this paper, we propose an acceleration technique of the adaptive filtering scheme called adaptive proximal forward-backward splitting method. For accelerating the convergence rate, the proposed method includes a step to shift the current estimate in the direction of the difference between the cur...

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Vydané v:2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) s. 4296 - 4299
Hlavní autori: Yamagishi, Masao, Yukawa, Masahiro, Yamada, Isao
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.05.2011
Predmet:
Acceleration
Adaptive systems
Convergence
Noise
parallel projection
Projection algorithms
proximal forward-backward splitting method
sparse system identification
Steady-state
variable metric
ISBN:9781457705380, 1457705389
ISSN:1520-6149
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Abstract In this paper, we propose an acceleration technique of the adaptive filtering scheme called adaptive proximal forward-backward splitting method. For accelerating the convergence rate, the proposed method includes a step to shift the current estimate in the direction of the difference between the current and previous estimates based on the Fast Iterative Shrinkage/Thresholding Algorithm (FISTA). The computational complexity for this additional step is fairly low compared to the overall complexity of the algorithm. As an example of the proposed method, we derive an acceleration of the composition of the Adoptively Weighted Soft-Thresholding (AWST) operator and the exponentially weighted adaptive parallel projection. AWST shrinks the estimated filter coefficients to zero for exploiting the sparsity of the system to be estimated and the exponentially weighted adaptive parallel projection algorithm realizes high accuracy by utilizing all available information at each iteration. This accelerated method improves the steady-state mismatch drastically with its con vergence speed as fast as the proportionate affine projection algorithm.
AbstractList In this paper, we propose an acceleration technique of the adaptive filtering scheme called adaptive proximal forward-backward splitting method. For accelerating the convergence rate, the proposed method includes a step to shift the current estimate in the direction of the difference between the current and previous estimates based on the Fast Iterative Shrinkage/Thresholding Algorithm (FISTA). The computational complexity for this additional step is fairly low compared to the overall complexity of the algorithm. As an example of the proposed method, we derive an acceleration of the composition of the Adoptively Weighted Soft-Thresholding (AWST) operator and the exponentially weighted adaptive parallel projection. AWST shrinks the estimated filter coefficients to zero for exploiting the sparsity of the system to be estimated and the exponentially weighted adaptive parallel projection algorithm realizes high accuracy by utilizing all available information at each iteration. This accelerated method improves the steady-state mismatch drastically with its con vergence speed as fast as the proportionate affine projection algorithm.
Author Yamagishi, Masao
Yukawa, Masahiro
Yamada, Isao
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  surname: Yamagishi
  fullname: Yamagishi, Masao
  email: myamagi@sp.ss.titech.ac.jp
  organization: Dept. of Commun. & Integrated Syst., Tokyo Inst. of Technol., Tokyo, Japan
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  givenname: Masahiro
  surname: Yukawa
  fullname: Yukawa, Masahiro
  email: yukawa@eng.niigata-u.ac.jp
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  givenname: Isao
  surname: Yamada
  fullname: Yamada, Isao
  email: isao@sp.ss.titech.ac.jp
  organization: Dept. of Commun. & Integrated Syst., Tokyo Inst. of Technol., Tokyo, Japan
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Snippet In this paper, we propose an acceleration technique of the adaptive filtering scheme called adaptive proximal forward-backward splitting method. For...
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StartPage 4296
SubjectTerms Acceleration
Adaptive systems
Convergence
Noise
parallel projection
Projection algorithms
proximal forward-backward splitting method
sparse system identification
Steady-state
variable metric
Title Acceleration of adaptive proximal forward-backward splitting method and its application to sparse system identification
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