Multi Task Multi View Learning Based on Cooperative Multi Objective Optimization
Traditional multi task multi view (MTMV) models work under the single objective learning framework and cannot incorporate too many regularization terms, which are primarily attributed to the utilization of the conventional numerical optimization methods. To this end, a cooperative multi objective MTMV (CMO MTMV) learning method is proposed in this paper. In CMO MTMV, the MTMV problem is formulated as a multi objective optimization problem. Compared with the existing single objective MTMV learning methods, the proposed CMO MTMV method integrates more relations, including task task, view view, instance instance, and feature feature relations as multiple objectives. An effective cooperative multi objective quantum behaved particle swarm optimization (CMOQPSO) algorithm is further developed to solve the multi objective optimization problem. The integration of a multi swarm scheme and a local communication strategy in CMOQPSO renders this algorithm efficient. The experimental results verify the superiority of the proposed CMO MTMV method compared with the several state of the art machine learning methods.
IEEE Access