A FAST FIXED-POINT ALGORITHM FOR INDEPENDENT COMPONENT ANALYSIS

We introduce a novel fast algorithm for independent component analysis , which can be used for blind source separation and feature extraction . We show how a neural network learning rule can be transformed into a fixed-point iteration, which provides an algorithm that is very simpl e, does not depend on any user-defined parameters, and is fast to conv erge to the most accurate solution allowed by the data. The algorithm finds, one at a time, all nongaussian independent components, regardle ss of their probability distributions. The computations can be perform ed in either batch mode or a semiadaptive manner. The convergence of t he algorithm is rigorously proved, and the convergence speed is shown to be cubic. Some comparisons to gradient-based algorithms are made, s howing that the new algorithm is usually 10 to 100 times faster, somet imes giving the solution in just a few iterations.