Random Features for Large-Scale Kernel Machines

  title={Random Features for Large-Scale Kernel Machines},
  author={Ali Rahimi and Benjamin Recht},
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. Our randomized features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shift-invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that… CONTINUE READING
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Random projection

A. Blum
margins, kernels, and feature-selection. LNCS, 3940:52–68 • 2006
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