Sparse Incremental Regression Modeling Using Correlation Criterion with Boosting Search


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.

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Description/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.

Item Type: Article
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 260565
Date Deposited: 23 Feb 2005
Last Modified: 27 Mar 2014 20:03
Publisher: IEEE Signal Processing Society
Further Information:Google Scholar
ISI Citation Count:4
URI: http://eprints.soton.ac.uk/id/eprint/260565

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