| Cart Total:
Menu
Matrix Linear Discriminant Analysis
  • Quality

Matrix Linear Discriminant Analysis

Publication:
Technometrics
Date:
May 2020
Issue:
Volume 62 Issue 2
Pages:
pp. 196-205
Author(s):
Hu, Wei, Shen, Weining, Zhou, Hua, Kong, Dehan
The copyright of this article is not held by ASQ.

Abstract

We propose a novel linear discriminant analysis (LDA) approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the equivalence of the conventional LDA and the ordinary least squares, we consider an efficient nuclear norm penalized regression that encourages a low-rank structure. Theoretical properties including a nonasymptotic 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.

*Supplemental material accessed online through Taylor & Francis.

ALREADY A MEMBER?    REGISTER