A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data

Zengchao Xu, Shan Luo () and Zehua Chen
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Zengchao Xu: Shanghai Jiao Tong University
Shan Luo: Shanghai Jiao Tong University
Zehua Chen: National University of Singapore

Sankhya A: The Indian Journal of Statistics, 2023, vol. 85, issue 1, No 16, 467 pages

Abstract: Abstract Matrix-variate data arise in many scientific fields such as face recognition, medical imaging, etc. Matrix data contain important structure information which can be ruined by vectorization. Methods incorporating the structure information into analysis have significant advantages over vectorization approaches. In this article, we consider the problem of two-class classification with high-dimensional matrix-variate data, and propose a novel portmanteau-local-feature discrimination (PLFD) method. This method first identifies local discrimination features of the matrix variate and then pools them together to construct a discrimination rule. We investigated the theoretical properties of the PLFD method and established its asymptotic optimality. We carried out extensive numerical studies including simulation and real data analysis to compare this method with other methods available in the literature, which demonstrate that the PLFD method has a great advantage over the other methods in terms of misclassification rate.

Keywords: Matrix-variate data; Classification; Feature selection; Asymptotic optimality.; Primary AMS62H; Secondary AMS62H30 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13171-021-00255-2

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