Entropy | |
Minimum Error Entropy Algorithms with Sparsity Penalty Constraints | |
Zongze Wu1  Siyuan Peng1  Wentao Ma2  Badong Chen2  Jose C. Principe2  | |
[1] School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, |
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关键词: sparse estimation; minimum error entropy; correntropy induced metric; mean square convergence; impulsive noise; | |
DOI : 10.3390/e17053419 | |
来源: mdpi | |
【 摘 要 】
Recently, sparse adaptive learning algorithms have been developed to exploit system sparsity as well as to mitigate various noise disturbances in many applications. In particular, in sparse channel estimation, the parameter vector with sparsity characteristic can be well estimated from noisy measurements through a sparse adaptive filter. In previous studies, most works use the mean square error (MSE) based cost to develop sparse filters, which is rational under the assumption of Gaussian distributions. However, Gaussian assumption does not always hold in real-world environments. To address this issue, we incorporate in this work an
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland
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