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Activity Number: 318
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #309633
Title: Semi-Supervised Model-Based Clustering with Regularized Covariance Matrix Estimation
Author(s): Brad Price*+ and Charles J. Geyer and Adam J. Rothman
Companies: University of Minnesota and University of Minnesota and University of Minnesota
Keywords: Model Based Clustering ; Semi-Supervised Learning ; Clustering ; Regularization ; Covariance Matrix Estimation
Abstract:

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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