Abstract Details
Activity Number:
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73
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #310130 |
Title:
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Sparse Laplacian Shrinkage for Inverse Covariance Estimation in Heterogeneous Sample
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Author(s):
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Takumi Saegusa*+ and Ali Shojaie
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Companies:
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University of Washington and University of Washington
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Keywords:
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graphical model ;
high dimensional ;
graph Laplacian ;
covariance matrix ;
network ;
alternating directions method of multipliers
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Abstract:
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We consider the problem of estimating multiple Gaussian graphical models from heterogeneous samples where the relationship among samples is given as a graph. This problem is motivated by estimation of gene networks for cancer patients with multiple subtypes, some of which are similar to each other but others of which are not. Estimation of a single graphical model in this problem blurs heterogeneity across samples while previously proposed methods in a similar problem often assume the equal level of resemblance among samples, resulting in inefficient (and misleading in some cases) use of information. We propose a method to jointly estimate multiple graphical models by borrowing strength across samples in estimating a common structure and exploiting resemblance information among samples given as a graph to make contrasts. This is achieved by combined use of the ell-1 penalty for the former and the graph Laplacian shrinkage penalty for the latter. We implement an ADMM algorithm to compute our estimator and illustrate its performance through simulations and real data sets.
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Authors who are presenting talks have a * after their name.
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