Real-time sufficient dimension reduction through principal least squares support vector machines

Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090, Dong, Yuexiao and Shin, Seung Jun 2021. Real-time sufficient dimension reduction through principal least squares support vector machines. Pattern Recognition 112 , 107768. 10.1016/j.patcog.2020.107768

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Abstract

We propose a real-time approach for sufficient dimension reduction. Compared with popular sufficient dimension reduction methods including sliced inverse regression and principal support vector machines, the proposed principal least squares support vector machines approach enjoys better estimation of the central subspace. Furthermore, this new proposal can be used in the presence of streamed data for quick real-time updates. It is demonstrated through simulations and real data applications that our proposal performs better and faster than existing algorithms in the literature.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	Elsevier
ISSN:	0031-3203
Date of First Compliant Deposit:	26 November 2020
Date of Acceptance:	25 November 2020
Last Modified:	18 Jan 2025 23:00
URI:	https://orca.cardiff.ac.uk/id/eprint/136625

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