Emergent Mind
Quadrature-based features for kernel approximation
(1802.03832)
Published Feb 11, 2018
in
cs.LG
and
stat.ML
Abstract
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 behaviour and conduct an extensive empirical study that supports our hypothesis.
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