Abstract:Kernel Methods are algorithms that are widely used, mainly because they can implicitly perform a non-linear mapping of the input data to a high dimensional feature space. In this paper, novel Kernel Matrices, that reflect the general structure of data, are proposed for classification. The proposed Matrices exploit properties of the graph theory, which are generated using power iterations of already known Kernel Matrices and three approaches are presented. Experiments on various datasets are conducted and stati… Show more
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