An Efficient Independent Component Analysis Algorithm for Sub-Gaussian Sources.

A crucial problem for on-line independent component analysis (ICA) algorithm is the choice of step-size, which reflects a tradeoff between steady-state error and convergence speed. This paper proposes a novel ICA algorithm for sub-Gaussian sources, which converges fast while maintaining low steady-state error, since it adopts some techniques, such as the introduction of innovation, usage of skewness information and variable step-size for natural gradient. Simulations have verified these approaches. [ABSTRACT FROM AUTHOR]