Title: Principal weighted least square support vector machine: An online dimension-reduction tool for binary classification
Authors: Seung Jun Shin - Korea University (Korea, South) [presenting]
Andreas Artemiou - Cardiff University (United Kingdom)
Abstract: As relevant technologies advance, steamed data that is continuously collected are frequently encountered in various applications, and the need for scalable algorithms becomes urgent. We propose the principal weighted least square support vector machine(PWLSSVM) as a novel tool for SDR in binary classification, in which most SDR methods suffer since they assume continuous $Y$. We further show that the PWLSSVM can be employed for the real-time 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 analysis.