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arXiv · 1809.08746

Matrix Linear Discriminant Analysis

Wei Hu·Weining Shen·Hua Zhou·Dehan Kong
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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.

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Wei Hu, Weining Shen, Hua Zhou, Dehan Kong. 2018-09-24. Matrix Linear Discriminant Analysis. https://arxiv.org/abs/1809.08746

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