Ondřej Chum presents Optimizing Explicit Feature Maps on Intervals

Approximating non-linear kernels by finite-dimensional feature maps is a
popular approach for accelerating training and evaluation of support vector
machines or to encode information into efficient match kernels. We propose a
novel method of data independent construction of low-dimensional feature maps.
The problem is formulated as a linear program that jointly considers two
competing
objectives:the quality of the approximation and the dimensionality of the
feature map. For both shift-invariant and homogeneous kernels the proposed
method achieves better approximation at the same dimensionality or comparable
approximations at lower dimensionality of the feature map compared with
state-of-the-art methods.