Layer-Wise Relevance Propagation Based Sample Condensation for Kernel Machines

Kernel machines are a powerful class of methods for classification and regression. Making kernel machines fast and scalable to large data, however, is still a challenging problem due to the need of storing and operating on the Gram matrix. In this paper we propose a novel approach to sample condensation for kernel machines, preferably without impairing the classification performance. To our best knowledge, there is no previous work with the same goal reported in the literature. For this purpose we make use of the neural network interpretation of kernel machines. Explainable AI techniques, in particular the Layer-wise Relevance Propagation method, are used to measure the relevance (importance) of training samples. Given this relevance measure, a decremental strategy is proposed for sample condensation. Experimental results on three data sets show that our approach is able to achieve the goal of substantial reduction of the number of training samples.