Poster
Quadrature-based features for kernel approximation
Marina Munkhoeva · Yermek Kapushev · Evgeny Burnaev · Ivan Oseledets
Room 517 AB #130
Keywords: [ Computational Complexity ] [ Kernel Methods ]
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.
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