Publication
Title
Exploiting multiplex data relationships in Support Vector Machines
Author
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 base kernels, 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.
Language
English
Source (journal)
Pattern recognition. - Oxford
Publication
Oxford : 2019
ISSN
0031-3203
DOI
10.1016/J.PATCOG.2018.07.032
Volume/pages
85 (2019) , p. 70-77
ISI
000447819300007
Full text (Publisher's DOI)
UAntwerpen
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 17.10.2023
Last edited 12.02.2024
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