On the Impact of Kernel Approximation on Learning Accuracy

Kernel approximation is commonly used to scale kernel-based algorithms to applications containing as many as several million instances. This paper analyzes the effect of such approximations in the kernel matrix on the hypothesis generated by several widely used learning algorithms. We give stability bounds based on the norm of the kernel approximation for these algorithms, including SVMs, KRR, and graph Laplacian-based regularization algorithms. These bounds help determine the degree of approximation that can be tolerated in the estimation of the kernel matrix. Our analysis is general and applies to arbitrary approximations of the kernel matrix. However, we also give a specific analysis of the Nystrom low-rank approximation in this context and report the results of experiments evaluating the quality of the Nystrom low-rank kernel approximation when used with ridge regression.