Flexible Independent Component Analysis
Seungjin Choi , Andrzej Cichocki , Shun-Ichi Amari
AbstractThis 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.
|Journal series||Journal of VLSI signal processing systems for signal, image and video technology, ISSN 0922-5773, 1573-109X|
|Keywords in English||Electronic and Computer Engineering, Signal, Image and Speech Processing|
|Citation count*||225 (2015-03-25)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.