An EM method for spatio-temporal blind source separation using an AR-MOG source model
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Cross-Entropy optimization for independent process analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Independent subspace analysis on innovations
ECML'05 Proceedings of the 16th European conference on Machine Learning
Dual multivariate auto-regressive modeling in state space for temporal signal separation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Autoregressive model of the hippocampal representation of events
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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It has been shown recently that the identification of mixed hidden independent auto-regressive processes (independent process analysis, IPA), under certain conditions, can be free from combinatorial explosion. The key is that IPA can be reduced (i) to independent subspace analysis and then, via a novel decomposition technique called Separation Theorem, (ii) to independent component analysis. Here, we introduce an iterative scheme and its neural network representation that takes advantage of the reduction method and can accomplish the IPA task. Computer simulation illustrates the working of the algorithm.