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BETA DIVERGENCE AND KERNELS METHODS WITH SUPPORT VECTOR MACHINE (SVM)
Keywords:
Hilbertian metrics, positive definite (pd) kernels, divergence, support vector machine (SVM).DOI:
https://doi.org/10.17654/0972086323006Abstract
In the field of statistical modelling, the distance or divergence measure is a criterion widely known and used tool for theoretical and applied statistical inference and data processing problems. In this paper, we deal with the well-known beta-divergences (referred to as $\beta$-divergences), which are a family of cost functions parametrized by one hyperparameter and its tight connections with the notions of Hilbertian metrics and positive definite (pd) kernels on probability measures. An attempt is made to describe this dissimilarity measure, which can be symmetrized using two relationships. We compute the degree of symmetry of the $\beta$-divergence on the basis of Hilbertian metrics. We investigate the desirable properties that the proposed approach needs to build a positive definite kernel corresponding to this symmetric $\beta$-divergence, and establish the effectiveness of our approach with experiments conducted on support vector machine (SVM).
We perform experiments using the conditionally defined positive $K$ and the kernel transformed $K^{(\beta)}$ and show that these kernels have the same proportion of errors for the Jeffrey divergence and the chi-square divergence.
Received: September 27, 2022
Accepted: December 26, 2022
Published: May 8, 2023
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