@CONFERENCE\{IMM2001-0826,
    author       = "T. Kolenda and L. K. Hansen and J. Larsen",
    title        = "Signal Detection using {ICA}: Application to Chat Room Topic Spotting",
    year         = "2001",
    keywords     = "{ICA,} {BIC,} component detection, chat room analysis, topic spotting",
    pages        = "540-545",
    booktitle    = "Third International Conference on Independent Component Analysis and Blind Source Separation",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/826-full.html",
    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."
}