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

IEEE Access

Hua Guo¹  Di Zhou²  Bin Jiang²  Jun Wang³  Yajun Li⁴ 

[1] Department of General Practice, Wuxi People&x2019;School of Design Art and Media, Nanjing University of Science and Technology, Nanjing, China;School of Digital Media, Jiangnan University, Wuxi, China;s Hospital, Wuxi, China;

关键词: Multi-task multi-view learning;    multi-objective optimization;    quantum-behaved particle swarm optimization;    multi-swarm strategy;   

DOI  :  10.1109/ACCESS.2017.2777888

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown