摘要
基于核学习的非线性映射能力,提出一种小波核广义方差的核独立成分分析算法WKGV-KICA.小波核函数具有近似正交,适用于信号局部分析的优点.与互信息相联系,将核广义方差作为对比函数对统计独立性进行衡量,可以获得理想的数学特性.将该算法应用于宽范围的盲源分离问题的实例中,并与现有算法进了比较.实验结果表明,WKGV-KICA算法在同等条件下的分离精度更高,而且性能更好.
Based on the nonlinear mapping ability of kernel learning,an algorithm of kernel independent component analysis based on wavelet kernel generalized variance(WKGV-KICA) is proposed.The wavelet kernel which is characterized by approximate orthogonality has the advantage in local signal analysis.Related to mutual information theory,the contrast function defined by kernel generalized variance(KGV) has desirable mathematical properties as the measure of statistical independence.The algorithm is applied to wide-ranging blind source separation problems and compared with existing algorithms.Experimental results show that WKGV-KICA algorithm can achieve higher separation accuracy and better properties under the same condition.
出处
《控制与决策》
EI
CSCD
北大核心
2013年第7期972-977,共6页
Control and Decision
基金
甘肃省财政厅基本业务费项目(620026)
甘肃省硕导项目(1104-09)
关键词
盲源信号分离
核独立成分分析
核广义方差
小波核
blind source separation(BSS)
kernel independent component analysis(KICA)
kernel generalized variance(KGV)
wavelet kernel