Abstract
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.
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