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Orthogonal Least Square with Boosting for Regression

Orthogonal Least Square with Boosting for Regression
Orthogonal Least Square with Boosting for Regression
A novel technique is presented to construct sparse regression models based on the orthogonal least square method with boosting. This technique tunes the mean vector and diagonal covariance matrix of individual regressor by incrementally minimizing the training mean square error. A weighted optimization method is developed based on boosting to append regressors one by one in an orthogonal forward selection procedure. Experimental results obtained using this technique demonstrate that it offers a viable alternative to the existing state-of-art kernel modeling methods for constructing parsimonious regression models.
3-540-22881-0
593-599
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200
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
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200
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., Wang, X.X. and Brown, D.J. (2004) Orthogonal Least Square with Boosting for Regression. Yang, Z.R., Everson, R. and Yin, H.J. (eds.) 5th International Conference on Data Engineering and Automated Learning, United Kingdom. 25 - 27 Aug 2004. pp. 593-599 .

Record type: Conference or Workshop Item (Paper)

Abstract

A novel technique is presented to construct sparse regression models based on the orthogonal least square method with boosting. This technique tunes the mean vector and diagonal covariance matrix of individual regressor by incrementally minimizing the training mean square error. A weighted optimization method is developed based on boosting to append regressors one by one in an orthogonal forward selection procedure. Experimental results obtained using this technique demonstrate that it offers a viable alternative to the existing state-of-art kernel modeling methods for constructing parsimonious regression models.

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More information

Published date: 2004
Additional Information: Springer LNCS 3177 The paper received the best paper award. Event Dates: August 25-27, 2004
Venue - Dates: 5th International Conference on Data Engineering and Automated Learning, United Kingdom, 2004-08-25 - 2004-08-27
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 259859
URI: https://eprints.soton.ac.uk/id/eprint/259859
ISBN: 3-540-22881-0
PURE UUID: 1e4e0eac-2108-4023-9b8e-0e4f4778fb63

Catalogue record

Date deposited: 30 Aug 2004
Last modified: 19 Jul 2019 22:39

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