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

Title & Authors
Practical Approach for Blind Algorithms Using Random-Order Symbol Sequence and Cross-Correntropy
Kim, Namyong;

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 $\small{O(N^2)}$. In the simulation results, the proposed method shows the same learning performance while reducing its heavy calculation burden significantly.
Keywords
cross-correntropy;MCC;random symbols;blind;PDF;
Language
Korean
Cited by
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2.
충격성 잡음에 강인한 코렌트로피 기반 블라인드 알고리듬의 성능분석,김남용;

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