Multi-output least-squares support vector regression machines

Multi-output regression aims at learning a mapping from a multivariate input feature space to a multivariate output space. Despite its potential usefulness, the standard formulation of the least-squares support vector regression machine (LS-SVR) cannot cope with the multi-output case. The usual procedure is to train multiple independent LS-SVR, thus disregarding the underlying (potentially nonlinear) cross relatedness among different outputs. To address this problem, inspired by the multi-task learning methods, this study proposes a novel approach, Multi-output LS-SVR (MLS-SVR), in multi-output setting. Furthermore, a more efficient training algorithm is also given. Finally, extensive experimental results validate the effectiveness of the proposed approach.

► We propose a novel multi-output regression approach, Multi-output LS-SVR (MLS-SVR).
► This approach considers the underlying (potentially nonlinear) cross relatedness among different outputs.
► A more efficient training algorithm is also given.
► The experimental results on corn and polymer data sets validate the effectiveness of the proposed approach.