Combining Multiple Kernels by Augmenting the Kernel Matrix
In this paper we present a novel approach to combining multiple kernels where the kernels are computed from different information channels. In contrast to traditional methods that learn a linear combination of n kernels of size m x m, resulting in m coefficients in the trained classifier, we propose a method that can learn n x m coefficients. This allows to assign different importance to the information channel per example rather than per kernel. We analyse the proposed kernel combination in empirical feature space and provide its geometrical interpretation. We validate the approach on both UCI datasets and an object recognition dataset, and demonstrate that it leads to classification improvements.