Sparse component analysis via dyadic cyclic descent

Sparse component analysis (SCA) is a widely used method for solving the blind source separation problem. We develop a new cyclic descent algorithm for SCA based on a dyadic expansion. To select the associated tuning parameter a method based on the Bayesian information criterion is developed. In simulations the new algorithm is compared with state of the art algorithms from the literature.