Minimax optimal high-dimensional classification using deep neural networks
Document Type
Article
Publication Date
12-1-2022
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.
Identifier
85134236689 (Scopus)
Publication Title
Stat
External Full Text Location
https://doi.org/10.1002/sta4.482
e-ISSN
20491573
Issue
1
Volume
11
Grant
DMS 1764280
Fund Ref
National Science Foundation
Recommended Citation
Wang, Shuoyang and Shang, Zuofeng, "Minimax optimal high-dimensional classification using deep neural networks" (2022). Faculty Publications. 2426.
https://digitalcommons.njit.edu/fac_pubs/2426
