非参数GKNN估计的高效独立成分分析算法

Efficient non-parametric GKNN independent component analysis algorithm

基于概率密度非参数估计的广义k-最近邻估计(GKNN)和线性独立成分分析(ICA)神经网络,提出了一种新的ICA非参数算法,实现了对源信号分布的全"盲"要求.传统的ICA算法不能分离一般的包括超高斯、亚高斯和非对称分布的杂系混合信号,因此它们需知道源信号的一些信息.基于GKNN的非参数密度估计直接由观测信号样本出发,实现了对分离信号评价函数的直接估计,从而在一定程度上解决了ICA算法中如何选取估计信号评价函数的难题.所提算法可以只用一种灵活的评价函数分离任意的杂系混合信号,该算法为ICA的更广泛应用铺平了道路.模拟实验从统计性质和计算时间说明了所提算法性能的优越性.

The non-parametric density estimation generalized k-nearest neighbor（GKNN） estimation based novel independent component analysis（ICA） algorithm which is fully blind to the sources is proposed using a linear ICA neural network. The proposed GKNN density estimation is directly evaluated from the original data samples, so it solves the important problem in ICA and blind source separation （BSS）： how to choose nonlinear functions as the probability density function（PDF） estimation of the sources. Moreover, the GKNN-ICA algorithm can separate the hybrid mixtures of source signals which include Gaussian, super Gaussian, sub-Gaussian, and symmetric distribution ones using only a flexible model and it is completely blind to the sources. The algorithm presented in this paper provides the way for wider applications of ICA methods to real world signal processing. Simulations confirm the effectiveness of the proposed algorithm.