Verification Under Increasing Dimensionality

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    Verification decisions are often based on second order statistics estimated from a set of samples. Ongoing growth of computational resources allows for considering more and more features, increasing the dimensionality of the samples. If the dimensionality is of the same order as the number of samples used in the estimation or even higher, then the accuracy of the estimate decreases significantly. In particular, the eigenvalues of the covariance matrix are estimated with a bias and the estimate of the eigenvectors differ considerably from the real eigenvectors. We show how a classical approach of verification in high dimensions is severely affected by these problems, and we show how bias correction methods can reduce these problems.
    Original languageUndefined
    Title of host publication20th International Conference on Pattern Recognition (ICPR 2010)
    Place of PublicationLos Alamitos, CA, USA
    PublisherIEEE Computer Society
    Number of pages4
    ISBN (Print)978-0-7695-4109-9
    Publication statusPublished - Aug 2010
    Event20th International Conference on Pattern Recognition 2010 - Istanbul Convention & Exhibition Centre, Istanbul, Turkey
    Duration: 23 Aug 201026 Aug 2010
    Conference number: 20

    Publication series

    PublisherIEEE Computer Society
    ISSN (Print)1051-4651


    Conference20th International Conference on Pattern Recognition 2010
    Abbreviated titleICPR 2010
    Internet address


    • METIS-271099
    • Bias correction
    • IR-74103
    • EWI-18676
    • General Statistical Analysis
    • SCS-Safety
    • High dimensional verification

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