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Principal weighted logistic regression for sufficient dimension reduction in binary classification

Authors
Kim, BoyoungShin, Seung Jun
Issue Date
6월-2019
Publisher
KOREAN STATISTICAL SOC
Keywords
Binary classification; Model-free feature extraction; Weighted logistic regression
Citation
JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.48, no.2, pp.194 - 206
Indexed
SCIE
SCOPUS
KCI
Journal Title
JOURNAL OF THE KOREAN STATISTICAL SOCIETY
Volume
48
Number
2
Start Page
194
End Page
206
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/64878
DOI
10.1016/j.jkss.2018.11.001
ISSN
1226-3192
Abstract
Sufficient dimension reduction (SDR) is a popular supervised machine learning technique that reduces the predictor dimension and facilitates subsequent data analysis in practice. In this article, we propose principal weighted logistic regression (PWLR), an efficient SDR method in binary classification where inverse-regression-based SDR methods often suffer. We first develop linear PWLR for linear SDR and study its asymptotic properties. We then extend it to nonlinear SDR and propose the kernel PWLR. Evaluations with both simulated and real data show the promising performance of the PWLR for SDR in binary classification. (C) 2018 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
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