Relative trust region learning for ICA

We present a new learning method, relative trust-region learning, where we incorporate the relative optimization technique (M. Zibulevsky, Proc. ICA, pp. 897-902, 2003) into the trust-region method. We apply this relative trust-region learning method to the problem of independent component analysis (ICA), which leads to the relative TR-ICA algorithm which turns out to be faster than Newton-type ICA algorithms as well as gradient-based ICA algorithms and to possess the equivariant property. Empirical comparisons with several existing ICA algorithms confirm the fast convergence of the relative TR-ICA algorithm.