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Sparse Incremental Regression Modeling Using Correlation Criterion with Boosting Search

Sparse Incremental Regression Modeling Using Correlation Criterion with Boosting Search
Sparse Incremental Regression Modeling Using Correlation Criterion with Boosting Search
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.
198-201
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
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. (2005) Sparse Incremental Regression Modeling Using Correlation Criterion with Boosting Search. IEEE Signal Processing Letters, 12 (3), 198-201.

Record type: Article

Abstract

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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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

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Date deposited: 23 Feb 2005
Last modified: 14 Mar 2024 06:39

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Contributors

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

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