The University of Southampton
University of Southampton Institutional Repository

Kernel density construction using orthogonal forward regression

Kernel density construction using orthogonal forward regression
Kernel density construction using orthogonal forward regression
An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.
3-540-22881-0
586-592
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Yang, Z.R.
b71f8582-83df-4c36-9925-136cdfd207f9
Everson, R.
229c6ebc-01dd-412c-9f65-3d0df6417655
Yin, H.J.
f26dcb60-0113-43f0-ae3c-f06cda840d54
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Yang, Z.R.
b71f8582-83df-4c36-9925-136cdfd207f9
Everson, R.
229c6ebc-01dd-412c-9f65-3d0df6417655
Yin, H.J.
f26dcb60-0113-43f0-ae3c-f06cda840d54

Chen, S., Hong, X. and Harris, C.J. (2004) Kernel density construction using orthogonal forward regression. Yang, Z.R., Everson, R. and Yin, H.J. (eds.) 5th International Conference on Intelligent Data Engineering and Automated Learning, Exeter, United Kingdom. 25 - 27 Aug 2004. pp. 586-592 .

Record type: Conference or Workshop Item (Paper)

Abstract

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.

Text
ideal04_1.pdf - Other
Download (110kB)
Text
ideal04aP.pdf - Other
Download (1MB)

More information

Published date: 2004
Additional Information: Springer LNCS 3177 Event Dates: August 25-27, 2004
Venue - Dates: 5th International Conference on Intelligent Data Engineering and Automated Learning, Exeter, United Kingdom, 2004-08-25 - 2004-08-27
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 259858
URI: http://eprints.soton.ac.uk/id/eprint/259858
ISBN: 3-540-22881-0
PURE UUID: 55982a76-ed05-4e0b-b65c-5f3b36fb5097

Catalogue record

Date deposited: 30 Aug 2004
Last modified: 14 Mar 2024 06:28

Export record

Contributors

Author: S. Chen
Author: X. Hong
Author: C.J. Harris
Editor: Z.R. Yang
Editor: R. Everson
Editor: H.J. Yin

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 http://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.

×