A quasi-optimally efficient algorithm for independent component analysis

We propose an incremental algorithm for independent component analysis (ICA), that is guided by the statistical efficiency. Starting from an ℓ^ℓ∞ norm sparseness measure contrast function, we derive the learning algorithm based on a winner-take-all learning mechanism. It avoids the optimization of high order non-linear functions or density estimation, which have been used by other ICA methods, such as negentropy approximation, infomax, and maximum likelihood estimation based methods. We show that when the latent independent random variables are super-Gaussian distributions, the network efficiently extracts the independent components. We observed a much faster convergence than with other ICA methods.