Title :
Newton method for the ICA mixture model
Author :
Palmer, J.A. ; Makeig, S. ; Kreutz-Delgado, K. ; Rao, B.D.
fDate :
March 31 2008-April 4 2008
Abstract :
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.
Keywords :
Hessian matrices; Newton method; gradient methods; independent component analysis; maximum likelihood estimation; probability; signal processing; EEC segmentation; Hessian matrix; ICA mixture model; Newton method; asymptotic stability; gradient method; probabilistic mixture framework; quasimaximum likelihood estimation; signal processing; Asymptotic stability; Bayesian methods; Brain modeling; Electroencephalography; Independent component analysis; Large-scale systems; Newton method; Sensor arrays; Signal processing algorithms; Vectors; Bayesian linear mixture model; EEG signal analysis; Independent Component Analysis; Newton method;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2008.4517982
