Trust-region learning for ICA
Heeyoul Choi, Sookjeong Kim, Seungjin Choi · 2005
A trust-region method is a quite attractive optimization technique, which finds a direction and a step size in an efficient and reliable manner with the help of a quadratic model of the objective function. It is, in general, faster than the steepest descent method and is free of a pre-selected constant learning rate. In addition to its convergence property (between linear and quadratic convergence), its stability is always guaranteed, in contrast to the Newton's method. We present an efficient implementation of the maximum likelihood independent component analysis (ICA) using the trust-region method, which leads to trust-region-based ICA (TR-ICA) algorithms. The useful behavior of our TR-ICA algorithms is confirmed through numerical experimental results.