The University of Southampton
University of Southampton Institutional Repository

An orthogonal forward regression technique for sparse kernel density estimation

An orthogonal forward regression technique for sparse kernel density estimation
An orthogonal forward regression technique for sparse kernel density estimation
Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights of the selected sparse model are finally updated using the multiplicative nonnegative quadratic programming algorithm, which ensures the nonnegative and unity constraints for the kernel weights and has the desired ability to reduce the model size further. Except for the kernel width, the proposed method has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Several examples demonstrate the ability of this simple regression-based approach to effectively construct a SKD estimate with comparable accuracy to that of the full-sample optimised PW density estimate.
0925-2312
931-943
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d

Chen, Sheng, Hong, X. and Harris, Chris J. (2008) An orthogonal forward regression technique for sparse kernel density estimation. Neurocomputing, 71 (4-6), 931-943.

Record type: Article

Abstract

Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights of the selected sparse model are finally updated using the multiplicative nonnegative quadratic programming algorithm, which ensures the nonnegative and unity constraints for the kernel weights and has the desired ability to reduce the model size further. Except for the kernel width, the proposed method has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Several examples demonstrate the ability of this simple regression-based approach to effectively construct a SKD estimate with comparable accuracy to that of the full-sample optimised PW density estimate.

Text
neuroc08chc.pdf - Version of Record
Restricted to Repository staff only
Request a copy
Text
NEUR2008-71-Jan - Author's Original
Download (1MB)

More information

Published date: 1 April 2008
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 265129
URI: http://eprints.soton.ac.uk/id/eprint/265129
ISSN: 0925-2312
PURE UUID: a2f37e2e-a04b-4b0b-a8d5-c54bc5ac4580

Catalogue record

Date deposited: 31 Jan 2008 12:01
Last modified: 21 Apr 2021 16:32

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 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.

×