Newton method for the ICA mixture model
Jason A. Palmer, Scott Makeig, Kenneth Kreutz-Delgado, Bhaskar D. Rao · 2008
We derive an asymptotic Newton algorithm for quasi-maximum likelihood estimation of the ICA mixture model, using the ordinary gradient and Hessian. The probabilistic mixture framework yields an algorithm that can accommodate non-stationary environments and arbitrary source densities. We prove asymptotic stability when the source models match the true sources. An example application to EEC segmentation is given.