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

Authors
Artemiou, AndreasDong, YuexiaoShin, Seung Jun
Issue Date
4월-2021
Publisher
ELSEVIER SCI LTD
Keywords
Central subspace; Ladle estimator; Online sliced inverse regression; Principal support vector machines; Streamed data
Citation
PATTERN RECOGNITION, v.112
Indexed
SCIE
SCOPUS
Journal Title
PATTERN RECOGNITION
Volume
112
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/49459
DOI
10.1016/j.patcog.2020.107768
ISSN
0031-3203
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. (c) 2020 Elsevier Ltd. All rights reserved.
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