Standardized Natural Gradient ICA Algorithm
Hua Teng · Journal of China West Normal University · 2007
This paper presents a standardized natural gradient ICA(Independent Component Analysis) algorithm through improving the natural gradient ICA algorithm based on Lie Group Invariance.The new algorithm introduces a standardizing factor which makes the absolute value of the determinant of parameter matrix equal to one.Therefore,the learning process is more stable and faster by restricting the drastic change of parameter matrix.In addition,the new algorithm is simpler by using standardizing factor to simplify the general criterion function.In BSS(Blind Signal Separation) simulation experiment,we have compared three algorithms including the general gradient ICA algorithm,the natural gradient ICA algorithm and the new algorithm.The results show the third is the best in the precision of restoring signals and converges fastest.