Component ordering in independent component analysis based on data power

A.J. Hendrikse, Lieuwe Jan Spreeuwers

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

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    Abstract

    With Independent Component Analysis (ICA) the objective is to separate multidimensional data into independent components. A well known problem in ICA is that since both the independent components and the separation matrix have to be estimated, neither the ordering nor the amplitudes of the components can be determined. One suggested method for solving these ambiguities in ICA is to measure the data power of a component, which indicates the amount of input data variance explained by an independent component. This method resembles the eigenvalue ordering of principle components. We will demonstrate theoretically and with experiments that strong sources can be estimated with higher accuracy than weak components. Based on the selection by data power, a method is developed for estimating independent components in high dimensional spaces. A test with synthetic data shows that the new algorithm can provide higher accuracy than the usual PCA dimension reduction.
    Original languageUndefined
    Title of host publicationProceedings of the 28th Symposium on Information Theory in the Benelux
    EditorsRaymond N.J. Veldhuis, R.N.J. Veldhuis, H.S. Cronie
    Place of PublicationEindhoven
    PublisherWerkgemeenschap voor Informatie- en Communicatietheorie (WIC)
    Pages211-218
    Number of pages8
    ISBN (Print)978-90-365-2509-1
    Publication statusPublished - 24 Jun 2007
    Event28th Symposium on Information Theory in the Benelux 2007 - Best Western Dish Hotel, Enschede, Netherlands
    Duration: 24 May 200725 May 2007
    Conference number: 28

    Publication series

    Name
    PublisherWerkgemeenschap voor Informatie- en Communicatietechniek
    NumberLNCS4549

    Conference

    Conference28th Symposium on Information Theory in the Benelux 2007
    CountryNetherlands
    CityEnschede
    Period24/05/0725/05/07

    Keywords

    • EWI-10856
    • METIS-241832
    • IR-64279
    • SCS-Safety

    Cite this

    Hendrikse, A. J., & Spreeuwers, L. J. (2007). Component ordering in independent component analysis based on data power. In R. N. J. Veldhuis, R. N. J. Veldhuis, & H. S. Cronie (Eds.), Proceedings of the 28th Symposium on Information Theory in the Benelux (pp. 211-218). Eindhoven: Werkgemeenschap voor Informatie- en Communicatietheorie (WIC).
    Hendrikse, A.J. ; Spreeuwers, Lieuwe Jan. / Component ordering in independent component analysis based on data power. Proceedings of the 28th Symposium on Information Theory in the Benelux. editor / Raymond N.J. Veldhuis ; R.N.J. Veldhuis ; H.S. Cronie. Eindhoven : Werkgemeenschap voor Informatie- en Communicatietheorie (WIC), 2007. pp. 211-218
    @inproceedings{535365103bd443819edc41acc344497d,
    title = "Component ordering in independent component analysis based on data power",
    abstract = "With Independent Component Analysis (ICA) the objective is to separate multidimensional data into independent components. A well known problem in ICA is that since both the independent components and the separation matrix have to be estimated, neither the ordering nor the amplitudes of the components can be determined. One suggested method for solving these ambiguities in ICA is to measure the data power of a component, which indicates the amount of input data variance explained by an independent component. This method resembles the eigenvalue ordering of principle components. We will demonstrate theoretically and with experiments that strong sources can be estimated with higher accuracy than weak components. Based on the selection by data power, a method is developed for estimating independent components in high dimensional spaces. A test with synthetic data shows that the new algorithm can provide higher accuracy than the usual PCA dimension reduction.",
    keywords = "EWI-10856, METIS-241832, IR-64279, SCS-Safety",
    author = "A.J. Hendrikse and Spreeuwers, {Lieuwe Jan}",
    year = "2007",
    month = "6",
    day = "24",
    language = "Undefined",
    isbn = "978-90-365-2509-1",
    publisher = "Werkgemeenschap voor Informatie- en Communicatietheorie (WIC)",
    number = "LNCS4549",
    pages = "211--218",
    editor = "Veldhuis, {Raymond N.J.} and R.N.J. Veldhuis and H.S. Cronie",
    booktitle = "Proceedings of the 28th Symposium on Information Theory in the Benelux",
    address = "Netherlands",

    }

    Hendrikse, AJ & Spreeuwers, LJ 2007, Component ordering in independent component analysis based on data power. in RNJ Veldhuis, RNJ Veldhuis & HS Cronie (eds), Proceedings of the 28th Symposium on Information Theory in the Benelux. Werkgemeenschap voor Informatie- en Communicatietheorie (WIC), Eindhoven, pp. 211-218, 28th Symposium on Information Theory in the Benelux 2007, Enschede, Netherlands, 24/05/07.

    Component ordering in independent component analysis based on data power. / Hendrikse, A.J.; Spreeuwers, Lieuwe Jan.

    Proceedings of the 28th Symposium on Information Theory in the Benelux. ed. / Raymond N.J. Veldhuis; R.N.J. Veldhuis; H.S. Cronie. Eindhoven : Werkgemeenschap voor Informatie- en Communicatietheorie (WIC), 2007. p. 211-218.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

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    N2 - With Independent Component Analysis (ICA) the objective is to separate multidimensional data into independent components. A well known problem in ICA is that since both the independent components and the separation matrix have to be estimated, neither the ordering nor the amplitudes of the components can be determined. One suggested method for solving these ambiguities in ICA is to measure the data power of a component, which indicates the amount of input data variance explained by an independent component. This method resembles the eigenvalue ordering of principle components. We will demonstrate theoretically and with experiments that strong sources can be estimated with higher accuracy than weak components. Based on the selection by data power, a method is developed for estimating independent components in high dimensional spaces. A test with synthetic data shows that the new algorithm can provide higher accuracy than the usual PCA dimension reduction.

    AB - With Independent Component Analysis (ICA) the objective is to separate multidimensional data into independent components. A well known problem in ICA is that since both the independent components and the separation matrix have to be estimated, neither the ordering nor the amplitudes of the components can be determined. One suggested method for solving these ambiguities in ICA is to measure the data power of a component, which indicates the amount of input data variance explained by an independent component. This method resembles the eigenvalue ordering of principle components. We will demonstrate theoretically and with experiments that strong sources can be estimated with higher accuracy than weak components. Based on the selection by data power, a method is developed for estimating independent components in high dimensional spaces. A test with synthetic data shows that the new algorithm can provide higher accuracy than the usual PCA dimension reduction.

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    KW - METIS-241832

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    Hendrikse AJ, Spreeuwers LJ. Component ordering in independent component analysis based on data power. In Veldhuis RNJ, Veldhuis RNJ, Cronie HS, editors, Proceedings of the 28th Symposium on Information Theory in the Benelux. Eindhoven: Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). 2007. p. 211-218