Explicit Approximations of the Gaussian Kernel

Andrew Cotter; Joseph Keshet; Nathan Srebro

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arXiv · 1109.4603

Explicit Approximations of the Gaussian Kernel

Andrew Cotter·Joseph Keshet·Nathan Srebro

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Abstract

We investigate training and using Gaussian kernel SVMs by approximating the kernel with an explicit finite- dimensional polynomial feature representation based on the Taylor expansion of the exponential. Although not as efficient as the recently-proposed random Fourier features [Rahimi and Recht, 2007] in terms of the number of features, we show how this polynomial representation can provide a better approximation in terms of the computational cost involved. This makes our "Taylor features" especially attractive for use on very large data sets, in conjunction with online or stochastic training.

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Andrew Cotter, Joseph Keshet, Nathan Srebro. 2011-09-21. Explicit Approximations of the Gaussian Kernel. https://arxiv.org/abs/1109.4603

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