Asymptotic Newton Method for the ICA Mixture Model with Adaptive Source Densities
نویسندگان
چکیده
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 can accommodate non-stationary environments and arbitrary source densities. We prove asymptotic stability when the source models match the true sources. An application to EEG segmentation is given. Index Terms Independent Component Analysis, Bayesian linear model, mixture model, Newton method, EEG
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