Signal Detection using ICA: Application to Chat Room Topic Spotting | Thomas Kolenda, Lars Kai Hansen, Jan Larsen
| | Abstract | Signal detection and pattern recognition for online grouping huge amounts of data and retrospective analysis is becoming increasingly important as knowledge based standards, such as XML and advanced MPEG, gain popularity. Independent component analysis (ICA) can be used to both cluster and detect signals with weak a priori assumptions in multimedia contexts. ICA of real world data is typically performed without knowledge of the number of non-trivial independent components, hence, it is of interest to test hypotheses concerning the number of components or simply to test whether a given set of components is significant relative to a ``white noise'' null hypothesis. It was recently proposed to use the so-called Bayesian information criterion (BIC) approximation, for estimation of such probabilities of competing hypotheses. Here, we apply this approach to the understanding of chat. We show that ICA can detect meaningful context structures in a chat room log file. | | Keywords | ICA, BIC, component detection, chat room analysis, topic spotting | | Type | Conference paper [With referee] | | Conference | Third International Conference on Independent Component Analysis and Blind Source Separation | | Year | 2001 pp. 540-545 | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Intelligent Signal Processing |
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