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基于部分独立分量分析的盲源分离 被引量：3

Blind signal separation based on the partially independent component analysis
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摘要 独立分量分析是近年来发展起来的一种可有效应用于盲源分离的多通道信号处理方法,对从观测信号中分离出信源信号有较好的性能.但独立分量分析方法的主要限制之一是信源信号统计独立,而大多数实际应用问题都不能保证这一点,使运用独立分量分析进行盲源分离的效果受到极大的影响.因此,提出了利用特征选择的方法近似获得信源信号中的独立分量,对这些分量上的观测信号运用已有的独立分量分析方法进行盲源分离,获得了较好的分离结果. The Independent Component Analysis (ICA) is a recently developed method for multi-signal processing and Blind Source Separation (BSS). However, its constraint on the sources that the sources are statistically independent of each other greatly limits its applications to BSS since the sources in most applications are not guaranteed to be independent. This paper presents a partially independent component analysis (PICA) method for BSS of dependent sources, where the approximately independent indices of the sources are selected with some feature selection method, and ICA is performed on the selected indices of the observations. A large number of simulations and a real world DNA microarray data experiment show great availability and effectiveness of the method presented here.
作者 张军英 刘利平
机构地区 西安电子科技大学计算机学院
出处 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2004年第3期334-337,361,共5页 Journal of Xidian University
基金 国家自然科学基金资助项目(60371044 60071026) 留学回国人员科研启动基金资助项目
关键词 独立分量分析 部分独立分量分析 特征选择 盲源分离 Computer simulation DNA Feature extraction Image processing Independent component analysis Mathematical models Signal processing
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献15

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二级参考文献24

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共引文献11

同被引文献31

  • 1张贤达,朱孝龙,保铮.Grading learning for blind source separation[J].Science in China(Series F),2003,46(1):31-44. 被引量：14
  • 2申丽岩,方滨,沈毅.基于负熵极大的独立分量分析方法[J].中北大学学报（自然科学版）,2005,26(6):396-399. 被引量：14
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引证文献3

二级引证文献10

西安电子科技大学学报
2004年 第3期

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