Orthogonal-least-squares regression: A unified approach for data modelling

Authors:
S. Chen;X. Hong;B. L. Luk;C. J. Harris
Affiliations:
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;School of Systems Engineering, University of Reading, Reading RG6 6AY, UK;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China;School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
Venue:
Neurocomputing
Year:
2009

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Abstract

A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test criteria is formulated within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic data-modelling approach for constructing parsimonious kernel models with excellent generalisation capability.