Multi-Task Multi-View Learning Based on Cooperative Multi-Objective Optimization

• Main Authors: Di Zhou, Jun Wang, Bin Jiang, Hua Guo, Yajun Li
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Multi-task multi-view learning; multi-objective optimization; quantum-behaved particle swarm optimization; multi-swarm strategy
• Online Access: https://ieeexplore.ieee.org/document/8123910/

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.