@article{f9f96fd22cf443cca5d03a7fce81d45c,
title = "Real-time sufficient dimension reduction through principal least squares support vector machines",
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.",
keywords = "Central subspace, Ladle estimator, Online sliced inverse regression, Principal support vector machines, Streamed data",
author = "Andreas Artemiou and Yuexiao Dong and Shin, {Seung Jun}",
note = "Funding Information: The authors would like to thank the associate editor and three anonymous reviewers whose comments greatly improved this article. Thanks also go to Zhanrui Cai for sharing the online SIR code. S. J. Shin's research was partially funded by the National Reasearch Foundation of Korea (NRF) grants 2018R1D1A1B07043034 and 2019R1A4A1028134 and by Korea University grant K1806401 . Funding Information: Dr. Yuexiao Dong is an Associate Professor at the Department of Statistical Science at Temple University. Dr. Dong received his Bachelor{\textquoteright}s degree in Mathematics from Tsinghua University. He obtained his Ph.D. from Pennsylvania State University in 2009. Dr. Dong{\textquoteright}s research focuses on sufficient dimension reduction and high-dimensional data analysis. His research articles have been published in top-tier journals such as The Annals of Statistics, Journal of the American Association, and Biometrika. His proposal “New Developments in Sufficient Dimension Reduction” has been funded by the National Science Foundation. Dr. Dong has served as an Associate Editor for the Journal of Systems Science and Complexity since 2015. Funding Information: Dr. Andreas Artemiou is a Senior Lecturer in Statistics at the School of Mathematics in Cardiff University. He obtained his B.Sc. from University of Cyprus and his M.Sc. and Ph.D. from Pennsylvania State University in 2010. His research interests include supervised and unsupervised dimension reduction methodology, statistical and machine learning, kernel methods and applications. His research has been funded by National Science Foundation, the London Mathematical Society, the GW4 Network and the Wellcome Trust. Publisher Copyright: {\textcopyright} 2020",
year = "2021",
month = apr,
doi = "10.1016/j.patcog.2020.107768",
language = "English",
volume = "112",
journal = "Pattern Recognition",
issn = "0031-3203",
publisher = "Elsevier Limited",
}