EigenGP: Gaussian process models with adaptive eigenfunctions

  title={EigenGP: Gaussian process models with adaptive eigenfunctions},
  author={Hao Peng and Yuan Qi},
Gaussian processes (GPs) provide a nonparametric representation of functions. However, classical GP inference suffers from high computational cost for big data and it is difficult to design nonstationary GP priors in practice. In this paper, we propose a sparse Gaussian process model, EigenGP, based on data-dependent eigenfunctions of a GP prior. The data-dependent eigenfunctions make the Gaussian process nonstationary and can be viewed as dictionary elements. We learn these dictionary elements… CONTINUE READING
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