A Bootstrap Approach to Eigenvalue Correction

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    Abstract

    Eigenvalue analysis is an important aspect in many data modeling methods. Unfortunately, the eigenvalues of the sample covariance matrix (sample eigenvalues) are biased estimates of the eigenvalues of the covariance matrix of the data generating process (population eigenvalues). We present a new method based on bootstrapping to reduce the bias in the sample eigenvalues: the eigenvalue estimates are updated in several iterations, where in each iteration synthetic data is generated to determine how to update the population eigenvalue estimates. Comparison of the bootstrap eigenvalue correction with a state of the art correction method by Karoui shows that depending on the type of population eigenvalue distribution, sometimes the Karoui method performs better and sometimes our bootstrap method.
    Original languageUndefined
    Title of host publicationNinth IEEE International Conference on Data Mining, 2009. ICDM '09.
    Place of PublicationPiscataway
    PublisherIEEE Computer Society
    Pages818-823
    Number of pages6
    ISBN (Print)978-1-4244-5242-2
    DOIs
    Publication statusPublished - 6 Dec 2009
    Event9th IEEE International Conference on Data Mining, ICDM 2009 - Miami Beach, United States
    Duration: 6 Dec 20099 Dec 2009
    Conference number: 9

    Publication series

    Name
    PublisherIEEE Computer Society Press

    Workshop

    Workshop9th IEEE International Conference on Data Mining, ICDM 2009
    Abbreviated titleICDM
    CountryUnited States
    CityMiami Beach
    Period6/12/099/12/09

    Keywords

    • METIS-265783
    • Bootstrapping
    • IR-69834
    • SCS-Safety
    • isotonic tree method
    • Eigenvalue correction
    • General Statistical Analysis
    • EWI-17390

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