randomfeatureapproximaties
Random feature approximations are techniques for converting kernel methods into scalable, explicit feature maps. The goal is to approximate a kernel function k(x, y) by a finite-dimensional inner product z(x) · z(y), where z(x) is a fixed feature map. This allows training linear models on the mapped features instead of using expensive kernel computations, enabling large-scale applications.
A prominent approach is the Random Fourier Features (RFF) method, introduced to approximate shift-invariant kernels such
Other random feature schemes include random binning features, which partition the input space with random grids
The quality of the approximation improves as D increases, with theoretical guarantees under suitable conditions. Practical