Locally regularised orthogonal least squares algorithm for the construction of sparse kernel regression models

Abstract

The paper proposes to combine orthogonal least squares (OLS) model selection with local regularisation for efficient sparse kernel data modelling. By assigning each orthogonal weight in the regression model with an individual regularisation parameter, the ability for the OLS model selection to produce a very parsimonious model with excellent generalisation performance is greatly enhanced.