Principal weighted least square support vector machine: An online dimension-reduction tool for binary classification

被引：3

作者：

Jang, Hyun Jung ^{[1
]}

Shin, Seung Jun ^{[1
]}

Artemiou, Andreas ^{[2
]}

机构：

[1] Korea Univ, Seoul, South Korea

[2] Cardiff Univ, Cardiff, Wales

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2023年 / 187卷

基金：

新加坡国家研究基金会;

关键词：

Streamed data; Online update; Sufficient dimension reduction; Weighted least square support sector machine; SLICED INVERSE REGRESSION; LOGISTIC-REGRESSION; CENTRAL SUBSPACE; MATRIX;

D O I：

10.1016/j.csda.2023.107818

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

As relevant technologies advance, streamed data are frequently encountered in various applications, and the need for scalable algorithms becomes urgent. In this article, we propose the principal weighted least square support vector machine (PWLSSVM) as a novel tool for SDR in binary classification where most SDR methods suffer since they assume continuous Y. We further show that the PWLSSVM can be employed for the online SDR for the streamed data. Namely, the PWLSSVM estimator can be directly updated from the new data without having old data. We explore the asymptotic properties of the PWLSSVM estimator and demonstrate its promising performance in terms of both estimation accuracy and computational efficiency for both simulated and real data.& COPY; 2023 Elsevier B.V. All rights reserved.

引用

页数：11

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