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Activity Number:
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354
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #303741 |
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Title:
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Pairwise Variable Selection for High-Dimensional Model-Based Clustering
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Author(s):
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Frank Jian Guo*+ and Elizaveta Levina and George Michailidis and Ji Zhu
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Companies:
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University of Michigan and University of Michigan and University of Michigan and University of Michigan
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Address:
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Department of Statistics, Ann Arbor, MI, 48109,
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Keywords:
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Pairwise fusion ; Gaussian mixture models ; Variable selection ; EM algorithm ; Model-based clustering
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Abstract:
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Variable selection for clustering is an important and challenging problem in high-dimensional data analysis. Existing variable selection methods for model-based clustering select informative variables in a "one-in-all-out" manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if none of the clusters are separable by this variable. In many applications, however, it is of interest to further establish exactly which clusters are separable by each informative variable. To address this question, we propose a pairwise variable selection method for high-dimensional model-based clustering. The method is based on a new pairwise penalty. Results on simulated and real data show that the new method performs better than the L1-norm approach and offers better interpretation.
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