Exploiting multiplex data relationships in Support Vector Machines
作者：
Highlights：
• Introducing multiplex data relationships to the SVM optimization process.
• The proposed method exploits and extends the findings of Multiple Kernel Learning and graph-based SVM method families.
• The solution of the modified optimization problem lies in a regularized space, explicitly expressed as a linear combination of multiple single-graph regularized kernels.
• The contribution of each graph-regularized kernel to the SVM classification problem, is optimally estimated.
摘要
•Introducing multiplex data relationships to the SVM optimization process.•The proposed method exploits and extends the findings of Multiple Kernel Learning and graph-based SVM method families.•The solution of the modified optimization problem lies in a regularized space, explicitly expressed as a linear combination of multiple single-graph regularized kernels.•The contribution of each graph-regularized kernel to the SVM classification problem, is optimally estimated.
论文关键词：Multiplex data relationships,Support Vector Machine,Graph-based regularization,Multiple Kernel Learning
论文评审过程：Received 25 August 2017, Revised 30 May 2018, Accepted 31 July 2018, Available online 1 August 2018, Version of Record 11 August 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.07.032
