TY - GEN
T1 - Random Fourier features for kernel ridge regression
T2 - 34th International Conference on Machine Learning, ICML 2017
AU - Avron, Haim
AU - Kapralov, Michael
AU - Musco, Cameron
AU - Musco, Christopher
AU - Velingker, Ameya
AU - Zandieh, Amir
N1 - Publisher Copyright:
© 2017 International Machine Learning Society (IMLS). All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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 approximation, and show how spectra) matrix approximation bounds imply statistical guarantees for kernel ridge regression.
AB - 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 approximation, and show how spectra) matrix approximation bounds imply statistical guarantees for kernel ridge regression.
UR - https://www.scopus.com/pages/publications/85046992337
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AN - SCOPUS:85046992337
T3 - 34th International Conference on Machine Learning, ICML 2017
SP - 370
EP - 404
BT - 34th International Conference on Machine Learning, ICML 2017
PB - International Machine Learning Society (IMLS)
Y2 - 6 August 2017 through 11 August 2017
ER -