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Optimizing explicit feature maps on intervals
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DOI：10.1016/j.imavis.2017.07.001.png)
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Abstract
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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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Explicit feature maps
Shift-invariant kernels
Homogeneous kernels
Linear programming
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