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Abstract Details
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Activity Number:
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299
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #300748 |
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Title:
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Universal Dependency Prediction and Variable Selection with the Mira
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Author(s):
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Hesen Peng and Tianwei Yu *+
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Companies:
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Emory University
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Address:
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1518 Clifton Rd 3F, Atlanta, GA, 30322,
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Keywords:
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high-dimensional data ;
universal dependency ;
nonlinear ;
variable selection ;
prediction ;
Mira
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Abstract:
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The emergence of high-throughput data requires machine learning methods that accommodates universal types of dependency of arbitrary dimension. In this paper we propose the Mira score, a novel measure capable of identifying the existence of all types of probabilistic dependency (linear and nonlinear) of any dimension. Pre-Mira, a computationally efficient variable selection and prediction procedure is also proposed. Comparison and connection with existing method will also be provided.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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