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• W2050873513 abstract "Effective utilization of sparsity of the system to be estimated is a key to achieve excellent adaptive filtering performances. This can be realized by the adaptive proximal forward-backward splitting (APFBS) with carefully chosen parameters. In this paper, we propose a systematic parameter tuning based on a minimization principle of an unbiased MSE estimate. Thanks to the piecewise quadratic structure of the proposed MSE estimate, we can obtain its minimizer with low computational load. A numerical example demonstrates the efficacy of the proposed parameter tuning by its excellent performance over a broader range of SNR than a heuristic parameter tuning of the APFBS." @default.
• W2050873513 created "2016-06-24" @default.
• W2050873513 creator A5029561892 @default.
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• W2050873513 date "2014-05-01" @default.
• W2050873513 modified "2023-09-27" @default.
• W2050873513 title "Shrinkage tuning based on an unbiased MSE estimate for sparsity-aware adaptive filtering" @default.
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• W2050873513 doi "https://doi.org/10.1109/icassp.2014.6854650" @default.
• W2050873513 hasPublicationYear "2014" @default.
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