Hybrid linear and nonlinear complexity pursuit for blind source separation
Hybrid linear and nonlinear complexity pursuit for blind source separation
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DOI：
10.1016/j.cam.2012.03.022
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
Zhenwei Shi;Hongjuan Zhang;Zhi-guo Jiang
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文献类型：
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作者：
Zhenwei Shi;Hongjuan Zhang;Zhi-guo Jiang
盲源分离（BSS）是一种日益流行的数据分析技术，具有许多应用。已经提出了几种使用原始源的统计特性进行盲源分离的方法;对于一个著名的情况，非高斯性，这导致了独立分量分析（伊卡）。本文提出了一种基于线性和非线性复杂度追踪的混合盲源分离方法，该方法综合了源信号的三种统计特性：非高斯性、线性可预测性和非线性可预测性。提出了一种基于最小化损失函数的梯度学习算法。仿真结果验证了该方法的有效性。
Blind source separation (BSS) is an increasingly popular data analysis technique with many applications. Several methods for BSS using the statistical properties of original sources have been proposed; for a famous case, non-Gaussianity, this leads to independent component analysis (ICA). In this paper, we propose a hybrid BSS method based on linear and nonlinear complexity pursuit, which combines three statistical properties of source signals: non-Gaussianity, linear predictability and nonlinear predictability. A gradient learning algorithm is presented by minimizing a loss function. Simulations verify the efficient implementation of the proposed method.