Practical Approach for Blind Algorithms Using Random-Order Symbol Sequence and Cross-Correntropy 


Vol. 39,  No. 3, pp. 149-154, Mar.  2014


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  Abstract

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 O(N2). In the simulation results, the proposed method shows the same learning performance while reducing its heavy calculation burden significantly.

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  Cite this article

[IEEE Style]

N. Kim, "Practical Approach for Blind Algorithms Using Random-Order Symbol Sequence and Cross-Correntropy," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 3, pp. 149-154, 2014. DOI: .

[ACM Style]

Namyong Kim. 2014. Practical Approach for Blind Algorithms Using Random-Order Symbol Sequence and Cross-Correntropy. The Journal of Korean Institute of Communications and Information Sciences, 39, 3, (2014), 149-154. DOI: .

[KICS Style]

Namyong Kim, "Practical Approach for Blind Algorithms Using Random-Order Symbol Sequence and Cross-Correntropy," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 3, pp. 149-154, 3. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880