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Activity Number: 538 - SPEED: Predictive Analytics with Social/Behavioral Science Applications: Spatial Modeling, Education Assessment, Population Behavior, and the Use of Multiple Data Sources
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 11:15 AM
Sponsor: Section on Statistics in Imaging
Abstract #332641
Title: Matrix Linear Discriminant Analysis
Author(s): Wei Hu*
Companies: University of California, Irvine
Keywords: Linear discriminant analysis; Low rank; Matrix data; Nuclear norm; Risk bound

We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the equivalence of the conventional linear discriminant analysis and the ordinary least squares, we consider an efficient nuclear norm penalized regression that encourages a low-rank structure. Theoretical properties including a non-asymptotic risk bound and a rank consistency result are established. Simulation studies and an application to electroencephalography data show the superior performance of the proposed method over the existing approaches. This is joint work with Weining Shen(UCI), Dehan Kong(U Toronto) and Hua Zhou(UCLA).

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

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