Design of kernels for support multivector machines involving the Clifford geometric product and the conformal geometric neuron

This paper presents the design of kernels for nonlinear support vector machines using the Clifford geometric algebra framework. In this study we present the design of kernels involving the Clifford or geometric product making use of nonlinear mappings which map multi-vectors into higher dimensional geometric algebra. We introduce also the conformal geometric neuron for geometric classification. Experiments are given to demonstrate the usefulness of the approach.