In this article, we propose a multiview structural large margin classifier (MvSLMC) that simultaneously satisfies the consensus and complementarity principles in all views.  Specifically, on the one hand, MvSLMC employs a structural regularization term to promote cohesion within-class and separability between-class in each view.  Moreover, the introduction of hinge loss in MvSLMC results in sample sparsity, which we leverage to construct a safe screening rule (SSR) for accelerating MvSLMC.  Numerical experimental results demonstrate the effectiveness of MvSLMC and its safe acceleration method.
MvSLMC
In this article, we propose a multiview structural large margin classifier (MvSLMC) that simultaneously satisfies the consensus and complementarity principles in all views.  Specifically, on the one hand, MvSLMC employs a structural regularization term to promote cohesion within-class and separability between-class in each view.  Moreover, the introduction of hinge loss in MvSLMC results in sample sparsity, which we leverage to construct a safe screening rule (SSR) for accelerating MvSLMC.  Numerical experimental results demonstrate the effectiveness of MvSLMC and its safe acceleration method.
MvSLMC
In this article, we propose a multiview structural large margin classifier (MvSLMC) that simultaneously satisfies the consensus and complementarity principles in all views.  Specifically, on the one hand, MvSLMC employs a structural regularization term to promote cohesion within-class and separability between-class in each view.  Moreover, the introduction of hinge loss in MvSLMC results in sample sparsity, which we leverage to construct a safe screening rule (SSR) for accelerating MvSLMC.  Numerical experimental results demonstrate the effectiveness of MvSLMC and its safe acceleration method.
MvSLMC