Multi-task proximal support vector machine

• Propose highly efficient multi-task proximal support vector machine (MTPSVM).
• Develop a method to optimize the learning procedure of MTPSVM.
• Unbalanced MTPSVM is proposed to deal with the unbalanced sample problem.
• Propose proximal support vector regression (SVR) and multi-task proximal SVR.
• Extensive experiments demonstrate the effectiveness and efficiency of our MTPSVM.

With the explosive growth of the use of imagery, visual recognition plays an important role in many applications and attracts increasing research attention. Given several related tasks, single-task learning learns each task separately and ignores the relationships among these tasks. Different from single-task learning, multi-task learning can explore more information to learn all tasks jointly by using relationships among these tasks. In this paper, we propose a novel multi-task learning model based on the proximal support vector machine. The proximal support vector machine uses the large-margin idea as does the standard support vector machines but with looser constraints and much lower computational cost. Our multi-task proximal support vector machine inherits the merits of the proximal support vector machine and achieves better performance compared with other popular multi-task learning models. Experiments are conducted on several multi-task learning datasets, including two classification datasets and one regression dataset. All results demonstrate the effectiveness and efficiency of our proposed multi-task proximal support vector machine.