含噪独立分量分析的期望最大化算法

把期望最大化(EM)算法应用到含噪ICA模型中,即假定源信号具有统计独立性,并将其放在贝叶斯估计框架中,提出一种解决含噪独立分量分析(ICA)的期望最大化(EM)算法。在含噪ICA模型中,假设源信号的均值和方差服从更为一般的均匀分布,提出的EM算法将混合矩阵和超参数交替进行处理,可以有效地估计混合矩阵和超参数在一定模型下的模型参数,从而能够估计出源信号。仿真结果说明,该方法能够很好地解决含有噪声ICA模型下的盲源分离问题。

Expectation-Maximization Algorithm for Noisy Independent Component Analysis

Expectation-maximization (EM) algorithm is applied in the noisy independent component analysis (ICA) model, i.e., the source signals are assumed statistical independent and formulated in a Bayesian estimation framework. A Bayesian approach with EM algorithm for noisy ICA is proposed. In the noisy ICA model, supposing the means and variances of source signals are uniform, the proposed EM algorithm can efficiently estimate the model parameters of the mixing matrix and hyperparameters under a certain model, and then estimate the sources by processing the mixing matrix and hyperparameters alternatively. Simulation results show that the proposed method can perform blind source separation (BSS) with the noisy ICA model.