Layer-Wise Relevance Propagation Based Sample Condensation for Kernel Machines

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