Efficient variant of algorithm fastica for independent component analysis attaining the cramer-RAO lower bound

We propose an improved version of algorithm FastICA which is asymptotically efficient, i.e., its accuracy attains the Cramer-Rao lower bound provided that the probability distribution of the signal components belongs to the class of generalized Gaussian distribution. Its computational complexity is only slightly (about three times) higher than that of ordinary symmetric FastICA. Simulation section shows superior performance of the algorithm compared with JADE, and of non-parametric ICA