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  • On some Variants of the EM Algorithm for the Fitting of Finite Mixture Models

    Author(s)
    Ng, Shu-Kay
    J. McLachlan, Geoffrey
    Griffith University Author(s)
    Ng, Shu Kay Angus
    Year published
    2003
    Metadata
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    Abstract
    Finite mixture models are being increasingly used in statistical inference and to provide a model-based approach to cluster analysis. Mixture models can be fitted to independent data in a straightforward manner via the expectation-maximization (EM) algorithm. In this paper, we look at ways of speeding up the fitting of normal mixture models by using variants of the EM, including the so-called sparse and incremental versions. We also consider an incremental version based on imposing a multiresolution kd-tree structure on the data. Examples are given in which the EM algorithm can be speeded up by a factor of more than fifty ...
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    Finite mixture models are being increasingly used in statistical inference and to provide a model-based approach to cluster analysis. Mixture models can be fitted to independent data in a straightforward manner via the expectation-maximization (EM) algorithm. In this paper, we look at ways of speeding up the fitting of normal mixture models by using variants of the EM, including the so-called sparse and incremental versions. We also consider an incremental version based on imposing a multiresolution kd-tree structure on the data. Examples are given in which the EM algorithm can be speeded up by a factor of more than fifty for large data sets of small dimension.
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    Journal Title
    Austrian Journal of Statistics
    Volume
    32
    Issue
    1-2
    Publisher URI
    http://www.statistik.tuwien.ac.at/oezstat/
    Subject
    Statistics not elsewhere classified
    Statistics
    Publication URI
    http://hdl.handle.net/10072/33478
    Collection
    • Journal articles

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