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A variable step-size adaptive algorithm under maximum correntropy criterion | IEEE Conference Publication | IEEE Xplore

A variable step-size adaptive algorithm under maximum correntropy criterion


Abstract:

Correntropy, a novel localized similarity measure defined in kernel space, has been successfully used as a cost function in adaptive system training. The adaptive algorit...Show More

Abstract:

Correntropy, a novel localized similarity measure defined in kernel space, has been successfully used as a cost function in adaptive system training. The adaptive algorithms under the maximum correntropy criterion (MCC) have been shown to be robust to impulsive non-Gaussian noises. However, they may converge slowly especially at a region far from the optimal solution. In this paper, we propose a new MCC algorithm with a variable step-size (VSS) called the VSS-MCC algorithm, which may achieve a much faster convergence speed while maintaining similar steady-state performance. In the new algorithm, the step-size is updated based on an approximation for the curvature of performance surface. Simulation results demonstrate the superior performance of VSS-MCC compared with the original MCC algorithm.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
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Conference Location: Killarney, Ireland

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