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Minimum Error Entropy Algorithms with Sparsity Penalty Constraints
2015
Entropy
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
doi:10.3390/e17053419
fatcat:5lq42sh56rgaphqodhju6ic4hm