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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.
ac405529-3375-471a-8257-bda5c0d10e53
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.
ac405529-3375-471a-8257-bda5c0d10e53
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, 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.

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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, United Kingdom, 2004-08-25 - 2004-08-27
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 259858
URI: https://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: 18 Jul 2017 09:19

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