Random Walk Kernel Applications to Classification Using Support Vector Machines

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 statistical tests are performed, comparing our proposed approach against current Kernel Matrices used on support vector machines. Also, experiments on real datasets for folk dance and activity recognition that highlight the superiority of our proposed method, are provided.