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Practical Approach for Blind Algorithms Using Random-Order Symbol Sequence and Cross-Correntropy
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 Title & Authors
Practical Approach for Blind Algorithms Using Random-Order Symbol Sequence and Cross-Correntropy
Kim, Namyong;
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The cross-correntropy concept can be expressed with inner products of two different probability density functions constructed by Gaussian-kernel density estimation methods. Blind algorithms based on the maximization of the cross-correntropy (MCC) and a symbol set of randomly generated N samples yield superior learning performance, but have a huge computational complexity in the update process at the aim of weight adjustment based on the MCC. In this paper, a method of reducing the computational complexity of the MCC algorithm that calculates recursively the gradient of the cross-correntropy is proposed. The proposed method has only O(N) operations per iteration while the conventional MCC algorithms that calculate its gradients by a block processing method has . In the simulation results, the proposed method shows the same learning performance while reducing its heavy calculation burden significantly.
cross-correntropy;MCC;random symbols;blind;PDF;
 Cited by
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