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
2004
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.
.
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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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
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Contributors
Author:
S. Chen
Author:
X. Hong
Author:
C.J. Harris
Editor:
Z.R. Yang
Editor:
R. Everson
Editor:
H.J. Yin
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