电子科技大学学报Issue(2)：214-218,224,6.DOI:10.3969/j.issn.1001-0548.2013.02.007
基于相关熵的盲源分离算法
Blind Source Separation Algorithm Based on Correntropy
成昊 1唐斌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
A blind source separation algorithm based on correntropy is presented. Unlike the traditional independent component analysis (ICA) method which utilizes the forth-order statistics or temporal structure to achieve the blind source separation. This algorithm is motivated from the notion of correntropy in the information theoretic learning, utilizing the even statistics implied in correntropy. The cost function is established according to the relationship between the parametric centered correntropy and the independence measure, and then minimized by using the optimization algorithm to acquire the demixing matrix and separate the signal. Simulations show that the performance is better than the traditional ICA method when separating the mixture of the super-Gaussian source and sub-Gaussian source.关键词
盲源分离/相关熵/独立性测度/信息理论学习/参数化中心相关熵Key words
blind source separation/correntropy/independence measure/information theoretic learning/parametric centered correntropy分类
信息技术与安全科学引用本文复制引用
成昊,唐斌..基于相关熵的盲源分离算法[J].电子科技大学学报,2013,(2):214-218,224,6.
