The University of Southampton
University of Southampton Institutional Repository

Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D-optimality experimental design

Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D-optimality experimental design
Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D-optimality experimental design
The paper proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.
0018-9286
1029-1036
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, S., Hong, X. and Harris, C.J. (2003) Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D-optimality experimental design. IEEE Transactions on Automatic Control, 48 (6), 1029-1036.

Record type: Article

Abstract

The paper proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Postscript
robrols.ps - Other
Download (242kB)
PDF
01205199.pdf - Other
Download (470kB)

More information

Published date: June 2003
Additional Information: submitted for publication in June 2002
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 257755
URI: https://eprints.soton.ac.uk/id/eprint/257755
ISSN: 0018-9286
PURE UUID: d4f27fe7-87ab-4d3d-ab96-a9372e2868a0

Catalogue record

Date deposited: 26 Jun 2003
Last modified: 18 Jul 2017 09:36

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×