摘要
参考独立分量分析(ICA with Reference,ICA-R)充分利用先验知识或参考信号,取得了很好的分离效果,但其中的阈值参数很难选取,且计算量很大。理论分析和实验表明,若阈值选取不当,算法甚至不收敛。通过在FastICA算法的负熵对比度函数中引入ICA-R算法中的接近性度量函数作为正则化项,得到一个简单的改进算法。针对合成数据和实际的ECG数据的仿真实验表明,算法收敛快、提取效果好,同时正则化参数取值非常灵活。
Independent Component Analysis with Reference（ICA-R） utilizes a priori information or reference signal and achieves good separation results,but its threshold parameter is very hard to determine and its computation load is very great.Theoretic analysis and experiments shows ICA-R even can＇t converge if the threshold is improperly selected.By inserting the closeness measure function of ICA-R as a regularization term into the usual negentropy contrast function for FastICA,a very simple improved algorithm is proposed.Experiments with synthetic signals,real ECG data demonstrate its quick convergence,good separation and flexible selection for the regularization parameter as well as.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第10期138-140,共3页
Computer Engineering and Applications
基金
国家自然科学基金No.60736009~~
关键词
盲源分离
独立分量分析
盲源提取
参考独立分量分析
正则化
Blind Source Separation（BSS） Independent Component Analysis（ICA） Blind Source Extraction（BSE） Independent Component Analysis with Reference（ICA-R） regularization
