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

Andreas Artemiou, Yuexiao Dong, Seung Jun Shin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number107768
JournalPattern Recognition
Volume112
DOIs
Publication statusPublished - 2021 Apr

Keywords

  • Central subspace
  • Ladle estimator
  • Online sliced inverse regression
  • Principal support vector machines
  • Streamed data

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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