Blind Detection of Independent Dynamic Components

Lars Kai Hansen, Jan Larsen, Thomas Kolenda

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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    Abstract

    In certain applications of independent component analysis (ICA) it is 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 efficiently as an unbiased test set estimator.
    Original languageEnglish
    Title of host publicationIEEE International Conference on Acoustics, Speech, and Signal Processing 2001
    Volume5
    Publication date2001
    Pages3197-3200
    ISBN (Print)0-7803-7041-4
    DOIs
    Publication statusPublished - 2001
    EventIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2001) - Salt Lake City, UT, United States
    Duration: 7 May 200111 May 2001

    Conference

    ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2001)
    CountryUnited States
    CitySalt Lake City, UT
    Period07/05/200111/05/2001

    Bibliographical note

    Copyright: 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

    Keywords

    • Datamine
    • BIC
    • keywords
    • ICA
    • Chat room

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