Multi-output regression using a locally regularised orthogonal least-squares algorithm

The paper considers data modelling using multi-output regression models. A locally regularised orthogonal least-squares (LROLS) algorithm is proposed for constructing sparse multi-output regression models that generalise well. By associating each regressor in the regression model with an individual regularisation parameter, the ability of the multi-output orthogonal least-squares (OLS) model selection to produce a parsimonious model with a good generalisation performance is greatly enhanced.