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A Separable Maximum Correntropy Adaptive Algorithm
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DOI：10.1109/TCSII.2020.2977608.png)
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摘要
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In this brief, a separable maximum correntropy criterion (SMCC) algorithm is developed by exploiting the typical separability property of tensors. Utilizing the separability property, a great number savings are obtained along with accelerated learning rate and improved estimate accuracy. In the proposed SMCC, a correntropy scheme is used to construct a adaptive algorithm to combat the impulsive noise and outliers in non-Gaussian environment. The complexity and convergence analysis of the SMCC are presented and discussed. Examples with two-way matrix and three-way tensor are carried out to verify the performance of the proposed SMCC algorithm under mixture Gaussian and Studentx2019;s t noises.
Keyword:
Tensile stress
Signal processing algorithms
Convergence
Partitioning algorithms
Acceleration
Minimization
Computational complexity
Maximum correntropy criterion
tensor
separability
impulsive noise
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8.8K
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