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Local Regularization Assisted Orthogonal Least Squares Regression

Local Regularization Assisted Orthogonal Least Squares Regression
Local Regularization Assisted Orthogonal Least Squares Regression
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.
0925-2312
559-585
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53

Chen, S. (2006) Local Regularization Assisted Orthogonal Least Squares Regression. Neurocomputing, 69 (4-6), 559-585.

Record type: Article

Abstract

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.

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Published date: January 2006
Organisations: Southampton Wireless Group

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Local EPrints ID: 261620
URI: http://eprints.soton.ac.uk/id/eprint/261620
ISSN: 0925-2312
PURE UUID: f1ff6642-d3ab-49e0-a451-82eac0c39a59

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Date deposited: 02 Dec 2005
Last modified: 09 Dec 2019 20:14

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