Year: 2022
Author: Lican Kang, Yanyan Liu, Yuan Luo, Chang Zhu
East Asian Journal on Applied Mathematics, Vol. 12 (2022), Iss. 1 : pp. 35–52
Abstract
The hard thresholding regularised logistic regression in high dimensions with larger number of features than samples is considered. The sharp oracle inequality for the global solution is established. If the target signal is detectable, it is proven that with a high probability the estimated and true supports coincide. Starting with the KKT condition, we introduce the primal and dual active sets algorithm for fitting and also consider a sequential version of this algorithm with a warm-start strategy. Simulations and a real data analysis show that SPDAS outperforms LASSO, MCP and SCAD methods in terms of computational efficiency, estimation accuracy, support recovery and classification.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/eajam.110121.210621
East Asian Journal on Applied Mathematics, Vol. 12 (2022), Iss. 1 : pp. 35–52
Published online: 2022-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 18
Keywords: Sparse logistic regression hard thresholding regularisation PDAS SPDAS