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

Hyun Jung Jang, Seung Jun Shin and Andreas Artemiou

Computational Statistics & Data Analysis, 2023, vol. 187, issue C

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.

Keywords: Streamed data; Online update; Sufficient dimension reduction; Weighted least square support sector machine (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:187:y:2023:i:c:s0167947323001299

DOI: 10.1016/j.csda.2023.107818

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