Matrix Linear Discriminant Analysis
- May 2020
- Volume 62 Issue 2
- pp. 196-205
- Hu, Wei, Shen, Weining, Zhou, Hua, Kong, Dehan
The copyright of this article is not held by ASQ.
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.