An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation

This paper presents a novel approach, called adaptive polynomial power learning estimation (APPLE) based ICA algorithm, for independent component analysis (ICA) problem. In this algorithm, the form of separation nonlinearity is fixed at polynomial function, but the exponent is adaptive adjusted in implementation. Experiments have demonstrated that this algorithm can successfully separate the combinations of sub-Gaussian and super-Gaussian signals