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

Hyun Jung Jang, Seung Jun Shin, Andreas Artemiou

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number107818
JournalComputational Statistics and Data Analysis
Volume187
DOIs
Publication statusPublished - 2023 Nov

Bibliographical note

Funding Information:
S. J. Shin was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), grant number 2018R1D1A1B07043034 , 2022M3J6A1063595 , and 2023R1A2C1006587 .

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Online update
  • Streamed data
  • Sufficient dimension reduction
  • Weighted least square support sector machine

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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