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Sparse regression modelling using an incremental weighted optimization method based on boosting with correlation criterion

Sparse regression modelling using an incremental weighted optimization method based on boosting with correlation criterion
Sparse regression modelling using an incremental weighted optimization method based on boosting with correlation criterion
A novel technique is presented to construct sparse Gaussian regression models. Unlike most kernel regression modelling methods, which restrict kernel means to the training input data and use a fixed common variance for all the regressors, the proposed technique can tune the mean vector and diagonal covariance matrix of individual Gaussian regressor to best fit the training data based on the correlation between the regressor and the training data. An efficient repeated weighted optimization method is developed based on boosting with the correlation criterion to append regressors one by one in incremental regression modelling. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-art kernel modelling methods for constructing parsimonious regression models.
0 9533890 7 3
37-42
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200

Chen, S., Wang, X.X. and Brown, D.J. (2004) Sparse regression modelling using an incremental weighted optimization method based on boosting with correlation criterion. 10th Annual Conf. Chinese Automation and Computing Society in UK. pp. 37-42 .

Record type: Conference or Workshop Item (Other)

Abstract

A novel technique is presented to construct sparse Gaussian regression models. Unlike most kernel regression modelling methods, which restrict kernel means to the training input data and use a fixed common variance for all the regressors, the proposed technique can tune the mean vector and diagonal covariance matrix of individual Gaussian regressor to best fit the training data based on the correlation between the regressor and the training data. An efficient repeated weighted optimization method is developed based on boosting with the correlation criterion to append regressors one by one in incremental regression modelling. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-art kernel modelling methods for constructing parsimonious regression models.

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More information

Published date: 2004
Additional Information: Event Dates: Sept. 18, 2004
Venue - Dates: 10th Annual Conf. Chinese Automation and Computing Society in UK, 2004-09-18
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 260801
URI: https://eprints.soton.ac.uk/id/eprint/260801
ISBN: 0 9533890 7 3
PURE UUID: 2d3c5a92-c452-4b6f-9eb7-8a10a973415c

Catalogue record

Date deposited: 28 Apr 2005
Last modified: 18 Jul 2017 09:09

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