Matrix Linear Discriminant Analysis
Methodology
2019-05-06 v2 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1809.08746,
title = {Matrix Linear Discriminant Analysis},
author = {Wei Hu and Weining Shen and Hua Zhou and Dehan Kong},
journal= {arXiv preprint arXiv:1809.08746},
year = {2019}
}