On semiseparable kernels and efficient computation of regularized system identification and function estimation

Tianshi Chen*, Martin S. Andersen

*Corresponding author for this work

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

A long-standing problem for kernel-based regularization methods is their high computational complexity O(N3), where N is the number of data points. In this paper, we show that for semiseparable kernels and some typical input signals, their computational complexity can be lowered to O(Nq2), where q is the output kernel's semiseparability rank that only depends on the chosen kernel and the input signal.

Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume53
Issue number2
Pages (from-to)462-467
ISSN2405-8963
DOIs
Publication statusPublished - 2020
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020

Conference

Conference21st IFAC World Congress 2020
Country/TerritoryGermany
CityBerlin
Period12/07/202017/07/2020

Bibliographical note

Funding Information:
ChineseUniversityofHongKong,Shenzhen.Sthairnte-suepUgnriavnetrsiutyndoefrHcoonngtrKaoctngN,Soh.e2n0z1h4e.n0.003.23 funded by the a flernel. Unfortunately, it did not worfl. TΩen we find 2405-8963 Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2020.12.222

Publisher Copyright:
Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license

Keywords

  • Efficient computation
  • Kernel design
  • Kernel-based regularization
  • Semiseparable kernels
  • System identification

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