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arXiv · 2303.02470

Minimax optimal high-dimensional classification using deep neural networks

Shuoyang Wang·Zuofeng Shang
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Abstract

High-dimensional classification is a fundamentally important research problem in high-dimensional data analysis. In this paper, we derive a nonasymptotic rate for the minimax excess misclassification risk when feature dimension exponentially diverges with the sample size and the Bayes classifier possesses a complicated modular structure. We also show that classifiers based on deep neural networks can attain the above rate, hence, are minimax optimal.

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Shuoyang Wang, Zuofeng Shang. 2023-03-04. Minimax optimal high-dimensional classification using deep neural networks. https://doi.org/10.1002/sta4.482

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