Minimum Error Entropy Algorithms with Sparsity Penalty Constraints

Minimum Error Entropy Algorithms with Sparsity Penalty Constraints

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 measure...

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Bibliographic Details
Main Authors: Zongze Wu, Siyuan Peng, Wentao Ma, Badong Chen, Jose C. Principe
Format: Article
Language:English
Published: MDPI AG 2015-05-01
Series:Entropy
Subjects:
sparse estimation
minimum error entropy
correntropy induced metric
mean square convergence
impulsive noise
Online Access:http://www.mdpi.com/1099-4300/17/5/3419

Internet

http://www.mdpi.com/1099-4300/17/5/3419

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