  Sparse inducing points have long been a standard method to fit Gaussian
processes to big data. In the last few years, spectral methods that exploit
approximations of the covariance kernel have shown to be competitive. In this
work we exploit a recently introduced orthogonally decoupled variational basis
to combine spectral methods and sparse inducing points methods. We show that
the method is competitive with the state-of-the-art on synthetic and on
real-world data.
