Consensus and complementarity-based multi-view least square support vector machine [基于一致互补性的多视角最小二乘支持向量机]

作者：Tang J.^*; Li J.; Tian Y.

来源：Systems Engineering-Theory & Practice, 2022, 42(9): 2461-2471.

DOI：10.12011/SETP2021-0170

• complementarity principle
• consensus principle
• least square support vector machine
• multi-view learning

Multi-view learning (MVL) exploits the multi-view data to improve the performance of the learning tasks. However, most multi-view leaning models are built for only two-view setting, or mainly embed either the consensus principle or the complementarity principle. To overcome aforementioned drawbacks, we propose a consensus and complementarity-based multi-view least square support vector machine (MVLSSVM-2C), which leverages view-agreement on multi-view predictors and weight combination strategy. We then adopt an iterative two-step strategy to solve the optimization problem efficiently. Further more, the generalization capability is theoretically analyzed by using Rademacher complexity. The extensive experiments validate the effectiveness of the proposed model.

• 单位
  中国科学院大学