A Bootstrap Approach to Eigenvalue Correction

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    10 Citations (Scopus)
    67 Downloads (Pure)

    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

    Cite this

    Hendrikse, A. J., Spreeuwers, L. J., & Veldhuis, R. N. J. (2009). A Bootstrap Approach to Eigenvalue Correction. In Ninth IEEE International Conference on Data Mining, 2009. ICDM '09. (pp. 818-823). [10.1109/ICDM.2009.111] Piscataway: IEEE Computer Society. https://doi.org/10.1109/ICDM.2009.111
    Hendrikse, A.J. ; Spreeuwers, Lieuwe Jan ; Veldhuis, Raymond N.J. / A Bootstrap Approach to Eigenvalue Correction. Ninth IEEE International Conference on Data Mining, 2009. ICDM '09.. Piscataway : IEEE Computer Society, 2009. pp. 818-823
    @inproceedings{38ac18cda7f9452ea19dd180b04993c5,
    title = "A Bootstrap Approach to Eigenvalue Correction",
    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.",
    keywords = "METIS-265783, Bootstrapping, IR-69834, SCS-Safety, isotonic tree method, Eigenvalue correction, General Statistical Analysis, EWI-17390",
    author = "A.J. Hendrikse and Spreeuwers, {Lieuwe Jan} and Veldhuis, {Raymond N.J.}",
    note = "10.1109/ICDM.2009.111",
    year = "2009",
    month = "12",
    day = "6",
    doi = "10.1109/ICDM.2009.111",
    language = "Undefined",
    isbn = "978-1-4244-5242-2",
    publisher = "IEEE Computer Society",
    pages = "818--823",
    booktitle = "Ninth IEEE International Conference on Data Mining, 2009. ICDM '09.",
    address = "United States",

    }

    Hendrikse, AJ, Spreeuwers, LJ & Veldhuis, RNJ 2009, A Bootstrap Approach to Eigenvalue Correction. in Ninth IEEE International Conference on Data Mining, 2009. ICDM '09.., 10.1109/ICDM.2009.111, IEEE Computer Society, Piscataway, pp. 818-823, 9th IEEE International Conference on Data Mining, ICDM 2009, Miami Beach, United States, 6/12/09. https://doi.org/10.1109/ICDM.2009.111

    A Bootstrap Approach to Eigenvalue Correction. / Hendrikse, A.J.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

    Ninth IEEE International Conference on Data Mining, 2009. ICDM '09.. Piscataway : IEEE Computer Society, 2009. p. 818-823 10.1109/ICDM.2009.111.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    TY - GEN

    T1 - A Bootstrap Approach to Eigenvalue Correction

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    N2 - 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.

    AB - 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.

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    KW - SCS-Safety

    KW - isotonic tree method

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    KW - General Statistical Analysis

    KW - EWI-17390

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    DO - 10.1109/ICDM.2009.111

    M3 - Conference contribution

    SN - 978-1-4244-5242-2

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    BT - Ninth IEEE International Conference on Data Mining, 2009. ICDM '09.

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    Hendrikse AJ, Spreeuwers LJ, Veldhuis RNJ. A Bootstrap Approach to Eigenvalue Correction. In Ninth IEEE International Conference on Data Mining, 2009. ICDM '09.. Piscataway: IEEE Computer Society. 2009. p. 818-823. 10.1109/ICDM.2009.111 https://doi.org/10.1109/ICDM.2009.111