Blind Detection of Independent Dynamic Components

Lars Kai Hansen, Jan Larsen, Thomas Kolenda

AbstractIn certain applications of independent component analysis (ICA) it any of interest to test hypotheses concerning the number of components or simply to test whether a given number of components is significant relative to a ``white noise'' null hypothesis. We estimate probabilities of such competing hypotheses for ICA based on dynamic decorrelation. The probabilities are evaluated in the so-called Bayesian information criterion approximation, however, they are able to detect the content of dynamic components as efficient as an unbiased test set estimator.
KeywordsBIC, ICA, Chat room, keywords, Datamine
TypeConference paper [With referee]
ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing 2001
Year2001    Vol. 5    pp. 3197--3200
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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