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arXiv · 1710.10230

Not-So-Random Features

Brian Bullins·Cyril Zhang·Yi Zhang
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Abstract

We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels. Our method produces a sequence of feature maps, iteratively refining the SVM margin. We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game. Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.

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Brian Bullins, Cyril Zhang, Yi Zhang. 2017-10-27. Not-So-Random Features. https://arxiv.org/abs/1710.10230

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