This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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347
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract - #308600 |
Title:
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An Initialization Strategy for the EM Algorithm in Gaussian Mixtures
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Author(s):
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Igor Melnykov*+ and Volodymyr Melnykov
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Companies:
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Colorado State University and North Dakota State University
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Address:
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2200 Bonforte Blvd., Pueblo, CO, 81001, USA
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Keywords:
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EM algorithm ;
mixture models ;
Gaussian distribution ;
initialization
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Abstract:
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An approach is proposed for initializing the EM algorithm. As the EM algorithm is often sensitive to the choice of the initial parameter vector, efficient initialization is an important preliminary process for the future convergence of the algorithm to the best local maximum of the loglikelihood function. Currently, no initialization method has superiority over others in all practical settings. Considering Gaussian mixture models, we propose another strategy initializing mean vectors by choosing points with higher concentrations of neighbors and using this information for the preliminary estimation of dispersion matrices. The suggested approach is illustrated on several examples and compared with some other initialization methods.
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