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Activity Number:
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697
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #319617
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Title:
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High-Dimensional Matrix-Variate Linear Discriminant Analysis
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Author(s):
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Aaron Molstad* and Adam Rothman
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Companies:
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University of Minnesota and University of Minnesota
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Keywords:
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Optimization ;
Classification ;
Matrix-valued covariates ;
High-dimensional data
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Abstract:
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We propose a penalized likelihood method to perform matrix-variate linear discriminant analysis. Blockwise coordinate descent is used for the optimization. Fast approximations are also proposed.
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Authors who are presenting talks have a * after their name.
