Multiple Kernel LSSVM in Empirical Kernel Mapping Space

Abstract

Multiple kernel methods are superior to single kernel methods on treating multiple, heterogeneous data sources. Different from the existing multiple kernel methods which mainly work in implicit kernel space, we propose a novel multiple kernel method in empirical kernel mapping space. In empirical kernel mapping space, the combination of kernels can be treated as the weighted fusion of empirical kernel mapping samples. Based this fact, we developed a multiple kernel least squares support vector machine(LSSVM) to realize multiple kernel classification in empirical kernel mapping space. The experiments here illustrate that the proposed multiple LSSVM method is feasible and effective.