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• W2963173208 abstract "Kernel methods give powerful, flexible, and theoretically grounded approaches to solving many problems in machine learning. The standard approach, however, requires pairwise evaluations of a kernel function, which can lead to scalability issues for very large datasets. Rahimi and Recht (2007) suggested a popular approach to handling this problem, known as random Fourier features. The quality of this approximation, however, is not well understood. We improve the uniform error bound of that paper, as well as giving novel understandings of the embedding's variance, approximation error, and use in some machine learning methods. We also point out that surprisingly, of the two main variants of those features, the more widely used is strictly higher-variance for the Gaussian kernel and has worse bounds." @default.
• W2963173208 created "2019-07-30" @default.
• W2963173208 creator A5005191773 @default.
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• W2963173208 date "2015-07-12" @default.
• W2963173208 modified "2023-09-27" @default.
• W2963173208 title "On the error of random fourier features" @default.
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