Abstract Details
Activity Number:
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318
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #309633 |
Title:
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Semi-Supervised Model-Based Clustering with Regularized Covariance Matrix Estimation
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Author(s):
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Brad Price*+ and Charles J. Geyer and Adam J. Rothman
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Companies:
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University of Minnesota and University of Minnesota and University of Minnesota
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Keywords:
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Model Based Clustering ;
Semi-Supervised Learning ;
Clustering ;
Regularization ;
Covariance Matrix Estimation
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Abstract:
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We propose methods for clustering where a subset of the points have known labels. A penalized likelihood-based approach that incorporates regularized covariance matrix estimation is used, efficient algorithms are developed, and multiple methods to select the penalty parameters are studied. We compare our proposed methods to relevant competitors with simulations and a data example involving speech signals.
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Authors who are presenting talks have a * after their name.
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