TY - JOUR
T1 - Flexible independent component analysis
AU - Choi, Seungjin
AU - Cichocki, Andrzej
AU - Amari, Shun Ichi
PY - 2000/8
Y1 - 2000/8
N2 - This paper addresses an independent component analysis (ICA) learning algorithm with flexible nonlinearity, so named as flexible ICA, that is able to separate instantaneous mixtures of sub- and super-Gaussian source signals. In the framework of natural Riemannian gradient, we employ the parameterized generalized Gaussian density model for hypothesized source distributions. The nonlinear function in the flexible ICA algorithm is controlled by the Gaussian exponent according to the estimated kurtosis of demixing filter output. Computer simulation results and performance comparison with existing methods are presented.
AB - This paper addresses an independent component analysis (ICA) learning algorithm with flexible nonlinearity, so named as flexible ICA, that is able to separate instantaneous mixtures of sub- and super-Gaussian source signals. In the framework of natural Riemannian gradient, we employ the parameterized generalized Gaussian density model for hypothesized source distributions. The nonlinear function in the flexible ICA algorithm is controlled by the Gaussian exponent according to the estimated kurtosis of demixing filter output. Computer simulation results and performance comparison with existing methods are presented.
UR - https://www.scopus.com/pages/publications/0034244729
U2 - 10.1023/A:1008135131269
DO - 10.1023/A:1008135131269
M3 - 記事
AN - SCOPUS:0034244729
SN - 0922-5773
VL - 26
SP - 25
EP - 38
JO - Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
JF - Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
IS - 1
ER -