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Activity Number: 697
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319617
Title: High-Dimensional Matrix-Variate Linear Discriminant Analysis
Author(s): Aaron Molstad* and Adam Rothman
Companies: University of Minnesota and University of Minnesota
Keywords: Optimization ; Classification ; Matrix-valued covariates ; High-dimensional data

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.

Authors who are presenting talks have a * after their name.

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