A stochastic natural gradient descent algorithm for blind signal separation
Howard Hua Yang, S. Amari · 2002
A new blind separation algorithm is derived based on minimizing the mutual information of the output of the de-mixing system using natural gradient descent method. The algorithm can be easily implemented on a neural network with data dependent activation functions. A new performance function which depends only on the output and the de-mixing matrix is introduced. The new performance function is evaluated without any knowledge of the mixing matrix except for its order. It is very useful for comparing the performance of different blind separation algorithms. The performance of the new algorithm is compared to that of some existing blind separation algorithms by using the performance function. The new algorithm generally outperforms the existing algorithms because it minimizes the mutual information directly. This is verified by the simulation results.