Multi-task least-squares support vector machines

Abstract

There are often the underlying cross relatedness amongst multiple tasks, which is discarded directly by traditional single-task learning methods. Since multi-task learning can exploit these relatedness to further improve the performance, it has attracted extensive attention in many domains including multimedia. It has been shown through a meticulous empirical study that the generalization performance of Least-Squares Support Vector Machine (LS-SVM) is comparable to that of SVM. In order to generalize LS-SVM from single-task to multi-task learning, inspired by the regularized multi-task learning (RMTL), this study proposes a novel multi-task learning approach, multi-task LS-SVM (MTLS-SVM). Similar to LS-SVM, one only solves a convex linear system in the training phrase, too. What's more, we unify the classification and regression problems in an efficient training algorithm, which effectively employs the Krylow methods. Finally, experimental results on school and dermatology validate the effectiveness of the proposed approach.