The Experts below are selected from a list of 8844 Experts worldwide ranked by ideXlab platform
Isao Yamada - One of the best experts on this subject based on the ideXlab platform.
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sparsity aware adaptive filters based on l p norm inspired Soft Thresholding technique
International Symposium on Circuits and Systems, 2012Co-Authors: Masahiro Yukawa, Masao Yamagishi, Yuta Tawara, Isao YamadaAbstract:We propose a novel sparsity-aware adaptive filtering algorithm based on iterative use of weighted Soft-Thresholding. The weights are determined based on a rough local approximation of the l p norm (0 < p < 1). The proposed algorithm operates the weighted Soft-Thresholding for enhancing the sparsity, following estimation error managements with the affine projection. The proposed weighting technique alleviates an extra bias of no benefit caused by shrinking dominant coefficients. The numerical examples demonstrate that the proposed weighting technique outperforms the existing one when the situation changes under the fixed parameter settings.
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ISCAS - Sparsity-aware adaptive filters based on ℓ p -norm inspired Soft-Thresholding technique
2012 IEEE International Symposium on Circuits and Systems, 2012Co-Authors: Masahiro Yukawa, Masao Yamagishi, Yuta Tawara, Isao YamadaAbstract:We propose a novel sparsity-aware adaptive filtering algorithm based on iterative use of weighted Soft-Thresholding. The weights are determined based on a rough local approximation of the l p norm (0 < p < 1). The proposed algorithm operates the weighted Soft-Thresholding for enhancing the sparsity, following estimation error managements with the affine projection. The proposed weighting technique alleviates an extra bias of no benefit caused by shrinking dominant coefficients. The numerical examples demonstrate that the proposed weighting technique outperforms the existing one when the situation changes under the fixed parameter settings.
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a sparse adaptive filtering using time varying Soft Thresholding techniques
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yukihiro Murakami, Masao Yamagishi, Masahiro Yukawa, Isao YamadaAbstract:In this paper, we propose a novel adaptive filtering algorithm based on an iterative use of (i) the proximity operator and (ii) the parallel variable-metric projection. Our time-varying cost function is a weighted sum of squared distances (in a variable-metric sense) plus a possibly nonsmooth penalty term, and the proposed algorithm is derived along the idea of proximal forward-backward splitting in convex analysis. For application to sparse-system identification problems, we employ the (weighted) l 1 norm as the penalty term, leading to a time-varying Soft-Thresholding operator. As the simple example of the proposed algorithm, we present the variable-metric affine projection algorithm composed with the time-varying Soft-Thresholding operator. Numerical examples demonstrate that the proposed algorithms notably outperform their counterparts without Soft-Thresholding both in convergence speed and steady-state mismatch, while the extra computational complexity due to the additional Soft-Thresholding is negligibly low.
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ICASSP - A sparse adaptive filtering using time-varying Soft-Thresholding techniques
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yukihiro Murakami, Masao Yamagishi, Masahiro Yukawa, Isao YamadaAbstract:In this paper, we propose a novel adaptive filtering algorithm based on an iterative use of (i) the proximity operator and (ii) the parallel variable-metric projection. Our time-varying cost function is a weighted sum of squared distances (in a variable-metric sense) plus a possibly nonsmooth penalty term, and the proposed algorithm is derived along the idea of proximal forward-backward splitting in convex analysis. For application to sparse-system identification problems, we employ the (weighted) l 1 norm as the penalty term, leading to a time-varying Soft-Thresholding operator. As the simple example of the proposed algorithm, we present the variable-metric affine projection algorithm composed with the time-varying Soft-Thresholding operator. Numerical examples demonstrate that the proposed algorithms notably outperform their counterparts without Soft-Thresholding both in convergence speed and steady-state mismatch, while the extra computational complexity due to the additional Soft-Thresholding is negligibly low.
Masahiro Yukawa - One of the best experts on this subject based on the ideXlab platform.
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sparsity aware adaptive filters based on l p norm inspired Soft Thresholding technique
International Symposium on Circuits and Systems, 2012Co-Authors: Masahiro Yukawa, Masao Yamagishi, Yuta Tawara, Isao YamadaAbstract:We propose a novel sparsity-aware adaptive filtering algorithm based on iterative use of weighted Soft-Thresholding. The weights are determined based on a rough local approximation of the l p norm (0 < p < 1). The proposed algorithm operates the weighted Soft-Thresholding for enhancing the sparsity, following estimation error managements with the affine projection. The proposed weighting technique alleviates an extra bias of no benefit caused by shrinking dominant coefficients. The numerical examples demonstrate that the proposed weighting technique outperforms the existing one when the situation changes under the fixed parameter settings.
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ISCAS - Sparsity-aware adaptive filters based on ℓ p -norm inspired Soft-Thresholding technique
2012 IEEE International Symposium on Circuits and Systems, 2012Co-Authors: Masahiro Yukawa, Masao Yamagishi, Yuta Tawara, Isao YamadaAbstract:We propose a novel sparsity-aware adaptive filtering algorithm based on iterative use of weighted Soft-Thresholding. The weights are determined based on a rough local approximation of the l p norm (0 < p < 1). The proposed algorithm operates the weighted Soft-Thresholding for enhancing the sparsity, following estimation error managements with the affine projection. The proposed weighting technique alleviates an extra bias of no benefit caused by shrinking dominant coefficients. The numerical examples demonstrate that the proposed weighting technique outperforms the existing one when the situation changes under the fixed parameter settings.
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a sparse adaptive filtering using time varying Soft Thresholding techniques
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yukihiro Murakami, Masao Yamagishi, Masahiro Yukawa, Isao YamadaAbstract:In this paper, we propose a novel adaptive filtering algorithm based on an iterative use of (i) the proximity operator and (ii) the parallel variable-metric projection. Our time-varying cost function is a weighted sum of squared distances (in a variable-metric sense) plus a possibly nonsmooth penalty term, and the proposed algorithm is derived along the idea of proximal forward-backward splitting in convex analysis. For application to sparse-system identification problems, we employ the (weighted) l 1 norm as the penalty term, leading to a time-varying Soft-Thresholding operator. As the simple example of the proposed algorithm, we present the variable-metric affine projection algorithm composed with the time-varying Soft-Thresholding operator. Numerical examples demonstrate that the proposed algorithms notably outperform their counterparts without Soft-Thresholding both in convergence speed and steady-state mismatch, while the extra computational complexity due to the additional Soft-Thresholding is negligibly low.
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ICASSP - A sparse adaptive filtering using time-varying Soft-Thresholding techniques
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yukihiro Murakami, Masao Yamagishi, Masahiro Yukawa, Isao YamadaAbstract:In this paper, we propose a novel adaptive filtering algorithm based on an iterative use of (i) the proximity operator and (ii) the parallel variable-metric projection. Our time-varying cost function is a weighted sum of squared distances (in a variable-metric sense) plus a possibly nonsmooth penalty term, and the proposed algorithm is derived along the idea of proximal forward-backward splitting in convex analysis. For application to sparse-system identification problems, we employ the (weighted) l 1 norm as the penalty term, leading to a time-varying Soft-Thresholding operator. As the simple example of the proposed algorithm, we present the variable-metric affine projection algorithm composed with the time-varying Soft-Thresholding operator. Numerical examples demonstrate that the proposed algorithms notably outperform their counterparts without Soft-Thresholding both in convergence speed and steady-state mismatch, while the extra computational complexity due to the additional Soft-Thresholding is negligibly low.
Masao Yamagishi - One of the best experts on this subject based on the ideXlab platform.
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sparsity aware adaptive filters based on l p norm inspired Soft Thresholding technique
International Symposium on Circuits and Systems, 2012Co-Authors: Masahiro Yukawa, Masao Yamagishi, Yuta Tawara, Isao YamadaAbstract:We propose a novel sparsity-aware adaptive filtering algorithm based on iterative use of weighted Soft-Thresholding. The weights are determined based on a rough local approximation of the l p norm (0 < p < 1). The proposed algorithm operates the weighted Soft-Thresholding for enhancing the sparsity, following estimation error managements with the affine projection. The proposed weighting technique alleviates an extra bias of no benefit caused by shrinking dominant coefficients. The numerical examples demonstrate that the proposed weighting technique outperforms the existing one when the situation changes under the fixed parameter settings.
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ISCAS - Sparsity-aware adaptive filters based on ℓ p -norm inspired Soft-Thresholding technique
2012 IEEE International Symposium on Circuits and Systems, 2012Co-Authors: Masahiro Yukawa, Masao Yamagishi, Yuta Tawara, Isao YamadaAbstract:We propose a novel sparsity-aware adaptive filtering algorithm based on iterative use of weighted Soft-Thresholding. The weights are determined based on a rough local approximation of the l p norm (0 < p < 1). The proposed algorithm operates the weighted Soft-Thresholding for enhancing the sparsity, following estimation error managements with the affine projection. The proposed weighting technique alleviates an extra bias of no benefit caused by shrinking dominant coefficients. The numerical examples demonstrate that the proposed weighting technique outperforms the existing one when the situation changes under the fixed parameter settings.
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a sparse adaptive filtering using time varying Soft Thresholding techniques
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yukihiro Murakami, Masao Yamagishi, Masahiro Yukawa, Isao YamadaAbstract:In this paper, we propose a novel adaptive filtering algorithm based on an iterative use of (i) the proximity operator and (ii) the parallel variable-metric projection. Our time-varying cost function is a weighted sum of squared distances (in a variable-metric sense) plus a possibly nonsmooth penalty term, and the proposed algorithm is derived along the idea of proximal forward-backward splitting in convex analysis. For application to sparse-system identification problems, we employ the (weighted) l 1 norm as the penalty term, leading to a time-varying Soft-Thresholding operator. As the simple example of the proposed algorithm, we present the variable-metric affine projection algorithm composed with the time-varying Soft-Thresholding operator. Numerical examples demonstrate that the proposed algorithms notably outperform their counterparts without Soft-Thresholding both in convergence speed and steady-state mismatch, while the extra computational complexity due to the additional Soft-Thresholding is negligibly low.
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ICASSP - A sparse adaptive filtering using time-varying Soft-Thresholding techniques
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yukihiro Murakami, Masao Yamagishi, Masahiro Yukawa, Isao YamadaAbstract:In this paper, we propose a novel adaptive filtering algorithm based on an iterative use of (i) the proximity operator and (ii) the parallel variable-metric projection. Our time-varying cost function is a weighted sum of squared distances (in a variable-metric sense) plus a possibly nonsmooth penalty term, and the proposed algorithm is derived along the idea of proximal forward-backward splitting in convex analysis. For application to sparse-system identification problems, we employ the (weighted) l 1 norm as the penalty term, leading to a time-varying Soft-Thresholding operator. As the simple example of the proposed algorithm, we present the variable-metric affine projection algorithm composed with the time-varying Soft-Thresholding operator. Numerical examples demonstrate that the proposed algorithms notably outperform their counterparts without Soft-Thresholding both in convergence speed and steady-state mismatch, while the extra computational complexity due to the additional Soft-Thresholding is negligibly low.
Yukihiro Murakami - One of the best experts on this subject based on the ideXlab platform.
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a sparse adaptive filtering using time varying Soft Thresholding techniques
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yukihiro Murakami, Masao Yamagishi, Masahiro Yukawa, Isao YamadaAbstract:In this paper, we propose a novel adaptive filtering algorithm based on an iterative use of (i) the proximity operator and (ii) the parallel variable-metric projection. Our time-varying cost function is a weighted sum of squared distances (in a variable-metric sense) plus a possibly nonsmooth penalty term, and the proposed algorithm is derived along the idea of proximal forward-backward splitting in convex analysis. For application to sparse-system identification problems, we employ the (weighted) l 1 norm as the penalty term, leading to a time-varying Soft-Thresholding operator. As the simple example of the proposed algorithm, we present the variable-metric affine projection algorithm composed with the time-varying Soft-Thresholding operator. Numerical examples demonstrate that the proposed algorithms notably outperform their counterparts without Soft-Thresholding both in convergence speed and steady-state mismatch, while the extra computational complexity due to the additional Soft-Thresholding is negligibly low.
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ICASSP - A sparse adaptive filtering using time-varying Soft-Thresholding techniques
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yukihiro Murakami, Masao Yamagishi, Masahiro Yukawa, Isao YamadaAbstract:In this paper, we propose a novel adaptive filtering algorithm based on an iterative use of (i) the proximity operator and (ii) the parallel variable-metric projection. Our time-varying cost function is a weighted sum of squared distances (in a variable-metric sense) plus a possibly nonsmooth penalty term, and the proposed algorithm is derived along the idea of proximal forward-backward splitting in convex analysis. For application to sparse-system identification problems, we employ the (weighted) l 1 norm as the penalty term, leading to a time-varying Soft-Thresholding operator. As the simple example of the proposed algorithm, we present the variable-metric affine projection algorithm composed with the time-varying Soft-Thresholding operator. Numerical examples demonstrate that the proposed algorithms notably outperform their counterparts without Soft-Thresholding both in convergence speed and steady-state mismatch, while the extra computational complexity due to the additional Soft-Thresholding is negligibly low.
Xi-le Zhao - One of the best experts on this subject based on the ideXlab platform.
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two Soft Thresholding based iterative algorithms for image deblurring
Information Sciences, 2014Co-Authors: Jie Huang, Ting-zhu Huang, Xi-le ZhaoAbstract:Iterative regularization algorithms, such as the conjugate gradient algorithm for least squares problems (CGLS) and the modified residual norm steepest descent (MRNSD) algorithm, are popular tools for solving large-scale linear systems arising from image deblurring problems. These algorithms, however, are hindered by a semi-convergence behavior, in that the quality of the computed solution first increases and then decreases. In this paper, in order to overcome the semi-convergence behavior, we propose two iterative algorithms based on Soft-Thresholding for image deblurring problems. One of them combines CGLS with a denoising technique like Soft-Thresholding at each iteration and another combines MRNSD with Soft-Thresholding in a similar way. We prove the convergence of MRNSD and Soft-Thresholding based algorithm. Numerical results show that the proposed algorithms overcome the semi-convergence behavior and the restoration results are slightly better than those of CGLS and MRNSD with their optimal stopping iterations.
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Two Soft-Thresholding based iterative algorithms for image deblurring ☆
Information Sciences, 2014Co-Authors: Jie Huang, Ting-zhu Huang, Xi-le ZhaoAbstract:Iterative regularization algorithms, such as the conjugate gradient algorithm for least squares problems (CGLS) and the modified residual norm steepest descent (MRNSD) algorithm, are popular tools for solving large-scale linear systems arising from image deblurring problems. These algorithms, however, are hindered by a semi-convergence behavior, in that the quality of the computed solution first increases and then decreases. In this paper, in order to overcome the semi-convergence behavior, we propose two iterative algorithms based on Soft-Thresholding for image deblurring problems. One of them combines CGLS with a denoising technique like Soft-Thresholding at each iteration and another combines MRNSD with Soft-Thresholding in a similar way. We prove the convergence of MRNSD and Soft-Thresholding based algorithm. Numerical results show that the proposed algorithms overcome the semi-convergence behavior and the restoration results are slightly better than those of CGLS and MRNSD with their optimal stopping iterations.
