Exploiting multiplex data relationships in Support Vector Machines. (January 2019)

• Record Type: Journal Article
• Authors: Mygdalis, Vasileios; Tefas, Anastasios; Pitas, Ioannis
• Is Part Of: Pattern recognition. Volume 85(2019:Jan.)
• Page Start: 70
• Page End: 77
• Publication Date: 2019-01
• Subjects: Multiplex data relationships -- Support Vector Machine -- Graph-based regularization -- Multiple Kernel Learning; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.07.032
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract: In this paper, a novel method for introducing multiplex data relationships to the SVM optimization process is presented. Different properties about the training data are encoded in graph structures, in the form of pairwise data relationships. Then, they are incorporated to the SVM optimization problem, as modified graph-regularized basekernels, each highlighting a different property about the training data. The contribution of each graph-regularized kernel to the SVM classification problem, is estimated automatically. Thereby, the solution of the proposed modified SVM optimization problem lies in a regularized space, where data similarity is expressed by a linear combination of multiple single-graph regularized kernels. The proposed method exploits and extends the findings of Multiple Kernel Learning and graph-based SVM method families. It is shown that the available kernel options for the former can be broadened, and the exhaustive parameter tuning for the latter can be eliminated. Moreover, both method families can be considered as special cases of the proposed formulation, hereafter. Our experimental evaluation in visual data classification problems denote the superiority of the proposed method. The obtained classification performance gains can be explained by the exploitation of multiplex data relationships, during the classifier optimization process.