Minimax optimal high-dimensional classification using deep neural networks
Statistics Theory
2023-03-07 v1 Statistics Theory
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.
Cite
@article{arxiv.2303.02470,
title = {Minimax optimal high-dimensional classification using deep neural networks},
author = {Shuoyang Wang and Zuofeng Shang},
journal= {arXiv preprint arXiv:2303.02470},
year = {2023}
}