Random Fourier features is a technique for approximating inner products in the RKHS using a randomized feature map. It would be great to have this in MLKernels.
The kernel being approximated needs to have some specific properties. Mainly it needs to be stationary (shift-invariant) and scaled properly.
I need to look into this a bit more, but it seems like it might not be too difficult to implement. The main part being a function spectraldensity, which takes a kernel as an argument and returns a distribution to sample from.
Random Fourier features is a technique for approximating inner products in the RKHS using a randomized feature map. It would be great to have this in MLKernels.
Here's a paper introducing them: Random Features for Large-Scale Kernel Machines
A blog post demonstrating their use: Random Fourier Features for Kernel Density Estimation
The kernel being approximated needs to have some specific properties. Mainly it needs to be stationary (shift-invariant) and scaled properly.
I need to look into this a bit more, but it seems like it might not be too difficult to implement. The main part being a function
spectraldensity, which takes a kernel as an argument and returns a distribution to sample from.