Abstract Details
Activity Number:
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251
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #313013
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Title:
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Sparse Bayesian Learning (Empirical Bayes): High-Dimensional Regression and Hyperspectral Applications
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Author(s):
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Chia Chye Yee*+ and Yves Atchade
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Companies:
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and University of Michigan
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Keywords:
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Empirical Bayes ;
Hyperspectral Unmixing ;
Lasso ;
High Dimensional Statistics
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Abstract:
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In high dimensional statistics, the frequentist approach to the problem, such as lasso and its derivatives, is very successful and well established in terms of results and asymptotic theory. Meanwhile, bayesian methods involve using different priors as a method of implementing different penalization structure. While there is extensive literature dedicated to the lasso and bayesian methods, the empirical bayes approach in high dimensional regression remains relatively obscure. Sparse bayesian learning (SBL), introduced by Tipping (2001) is an empirical bayes method which combines elements from both methodology in the attempt to solve high dimensional problems. The poster aims to cover some preliminary analysis and the application of SBL to the problem of hyperspectral unmixing.
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Authors who are presenting talks have a * after their name.
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