“Know how to solve every problem that has been solved.” “What I cannot create, I do not understand.” — Richard Feynman

Random Fourier features give an explicit randomized feature map z(x) ∈ ℝ^D whose inner product z(x)·z(y) is an unbiased Monte Carlo estimate of a shift-invariant kernel k(x, y); this lets linear methods on z(x) emulate kernel methods at O(ND) cost instead of O(N²).

Claims

Random Fourier features give an explicit randomized feature map z(x) ∈ ℝ^D whose inner product z(x)·z(y) is an unbiased Monte Carlo estimate of a shift-invariant kernel k(x, y); this lets linear methods on z(x) emulate kernel methods at O(ND) cost instead of O(N²).

✓ supported derivational 90% draft mlkernel-methodsrandom-featuresapproximationfourier
id: random-fourier-features-approximate-kernel created: 2026-05-21 ← all claims