# Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees

@inproceedings{Avron2017RandomFF, title={Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees}, author={Haim Avron and Mikhail Kapralov and Cameron Musco and Christopher Musco and Ameya Velingker and Amir Zandieh}, booktitle={ICML}, year={2017} }

Random Fourier features is one of the most popular techniques for scaling up kernel methods, such as kernel ridge regression. However, despite impressive empirical results, the statistical properties of random Fourier features are still not well understood. In this paper we take steps toward filling this gap. Specifically, we approach random Fourier features from a spectral matrix approximation point of view, give tight bounds on the number of Fourier features required to achieve a spectral…

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