Local Regularization Assisted Orthogonal Least Squares Regression
Chen, S. (2006) Local Regularization Assisted Orthogonal Least Squares Regression. Neurocomputing, 69, (4-6), 559-585.
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A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsimonious or sparse regression models that generalize well. By associating each orthogonal weight in the regression model with an individual regularization parameter, the ability for the orthogonal least squares (OLS) model selection to produce a very sparse model with good generalization performance is greatly enhanced. Furthermore, with the assistance of local regularization, when to terminate the model selection procedure becomes much clearer. This LROLS algorithm has computational advantages over the recently introduced relevance vector machine (RVM) method.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||02 Dec 2005|
|Last Modified:||02 Mar 2012 00:20|
|Contributors:||Chen, S. (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||41|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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