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Sparse Incremental Regression Modeling Using Correlation Criterion with Boosting Search

Record type: Article

A novel technique is presented to construct sparse generalized Gaussian kernel regression models. The proposed method appends regressors in an incremental modeling by tuning the mean vector and diagonal covariance matrix of individual Gaussian regressor to best fit the training data based on a correlation criterion. It is shown that this is identical to incrementally minimize the modeling mean square error. The optimization at each regression stage is carried out with a simple search algorithm re-enforced by boosting. 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 models.

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Citation

Chen, S., Wang, X.X. and Brown, D.J. (2005) Sparse Incremental Regression Modeling Using Correlation Criterion with Boosting Search IEEE Signal Processing Letters, 12, (3), pp. 198-201.

More information

Published date: March 2005
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 260565
URI: http://eprints.soton.ac.uk/id/eprint/260565
PURE UUID: cbbf211f-28bb-4ecd-b19d-9197e2c990df

Catalogue record

Date deposited: 23 Feb 2005
Last modified: 18 Jul 2017 09:12

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Contributors

Author: S. Chen
Author: X.X. Wang
Author: D.J. Brown

University divisions


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