Blind source separation algorithm based on WKGV-KICA algorithm
Guo Lin · Kongzhi yu juece · 2013
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.