Random Fourier Features in Near-Linear Time

Random Fourier features approximate a shift-invariant kernel in ${\mathbb{R}}^d$, such as the Gaussian kernel $\exp (-\frac{1}{2}\Vert x-y{\Vert }_2^2)$, using independent samples from its spectral measure to construct an unbiased estimator for the kernel value with subgaussian concentration. However, generating $m$ such features requires $O(md)$ time. A long line of work on structured random features has accelerated this computation for the Gaussian kernel and other radial kernels, albeit with weaker concentration guarantees. We close this gap by showing a random feature map that can be computed in near-linear time $O((d+m)\log d)$ and yields near-subgaussian concentration. We obtain it through a new and tighter analysis of the Fastfood feature map of Le, Sarl\'os and Smola (ICML 2013), using Brascamp-Lieb inequalities and the Schoenberg representation of radial kernels.