NIPS Proceedingsβ

Quadrature-based features for kernel approximation

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings

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Conference Event Type: Poster


We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. Based on an efficient numerical integration technique, we propose a unifying approach that reinterprets the previous random features methods and extends to better estimates of the kernel approximation. We derive the convergence behavior and conduct an extensive empirical study that supports our hypothesis.