电子科技大学学报2012，Vol.41Issue(4)：527-531,5.DOI:10.3969/j.issn.1001-0548.2012.04.009
含噪独立分量分析的期望最大化算法
Expectation-Maximization Algorithm for Noisy Independent Component Analysis
张和发 1李立萍1
作者信息
- 1. 电子科技大学电子工程学院 成都611731
- 折叠
摘要
Abstract
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.关键词
贝叶斯方法/盲源分离/期望最大化算法/独立分量分析Key words
Bayesian approach/ blind source separation/ expectation-maximum algorithm/ independent component analysis分类
信息技术与安全科学引用本文复制引用
张和发,李立萍..含噪独立分量分析的期望最大化算法[J].电子科技大学学报,2012,41(4):527-531,5.
