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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Computational statistics 14 (1999), S. 469-489 
    ISSN: 1613-9658
    Keywords: Key words: Dimensionality reduction; EM algorithm; Finite mixtures; Fisher's linear discriminant; Nearest neighbors; Pattern recognition; Principal components
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Summary In automatic pattern recognition applications, numerous features that describe the classes are obtained in an attempt to ensure accurate classification of unknown observations. These features or dimensions must be reduced to a smaller number before classification schemes can be applied, because classifiers become computationally and analytically unmanageable in high dimensions. Principal components and Fisher's Linear Discriminant offer global dimensionality reduction within the framework of linear algebra applied to covariance matrices. This report describes local methods that use both mixture models and nearest neighbor calculations to construct local versions of these methods. These new versions for local dimensionality reduction will provide increased classification accuracy in lower dimensions.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Book
    Book
    Hoboken, N.J. :Wiley-Interscience,
    Title: Random graphs for statistical pattern recognition /
    Author: Marchette, David J.
    Publisher: Hoboken, N.J. :Wiley-Interscience,
    Year of publication: 2004
    Pages: XIII, 237 S. : , Ill., graph. Darst.
    Series Statement: Wiley series in probability and statistics
    ISBN: 0-471-22176-7
    Type of Medium: Book
    Language: English
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