Fastfood: Approximate Kernel Expansions in Loglinear Time

@article{Le2014FastfoodAK,
  title={Fastfood: Approximate Kernel Expansions in Loglinear Time},
  author={Quoc V. Le and Tam{\'a}s Sarl{\'o}s and Alexander J. Smola},
  journal={CoRR},
  year={2014},
  volume={abs/1408.3060}
}
Despite their successes, what makes kernel methods difficult to use in many large scale problems is the fact that storing and computing the decision function is typically expensive, especially at prediction time. In this paper, we overcome this difficulty by proposing Fastfood, an approximation that accelerates such computation significantly. Key to Fastfood is the observation that Hadamard matrices, when combined with diagonal Gaussian matrices, exhibit properties similar to dense Gaussian… CONTINUE READING
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