Exterior Penalty Function Method Based ICA Algorithm for Hybrid Sources Using GKNN Estimation

机译：基于GKNN估计的混合源ICA算法的外部罚函数法。

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Novel Independent Component analysis(ICA) algorithm for hybrid sources separation based on constrained optimization-exterior penalty function method is proposed. The proposed exterior penalty ICA algorithm is under the framework of constrained ICA(cICA) method to solve the constrained optimization problem by using the exterior penalty function method. In order to choose nonlinear functions as the probability density function(PDF) estimation of the source signals, generalized κ-nearest neighbor(GKNN) PDF estimation is proposed which can separate the hybrid mixtures of source signals using only a flexible model and more important it is completely blind to the sources. The proposed EX-cICA algorithm provides the way to wider applications of ICA methods to real world signal processing. Simulations confirm the effectiveness of the proposed algorithm.

机译：提出了一种基于约束优化-外部罚函数法的混合源分离新的独立分量分析算法。提出的外部惩罚ICA算法是在约束ICA（cICA）方法框架下，通过外部惩罚函数方法解决约束优化问题。为了选择非线性函数作为源信号的概率密度函数（PDF）估计，提出了广义κ最近邻PDF估计，该估计可以仅使用灵活模型来分离源信号的混合混合，更重要的是完全不了解消息来源。提出的EX-cICA算法为将ICA方法更广泛地应用于现实世界的信号处理提供了方法。仿真证实了该算法的有效性。

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来源
《Neural Information Processing pt.1; Lecture Notes in Computer Science; 4232》|2006年|1107-1116|共10页
会议地点 Hong Kong(CN)
作者
Fasong Wang; Hongwei Li; Rui Li;
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作者单位

School of Mathematics and Physics, China University of Geosciences Wuhan 430074, P.R. China;

School of Mathematics and Physics, Henan University of Technology Zhengzhou 450052, P.R. China;

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原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
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Exterior Penalty Function Method Based ICA Algorithm for Hybrid Sources Using GKNN Estimation

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