  Gaussian process models are commonly used as emulators for computer
experiments. However, developing a Gaussian process emulator can be
computationally prohibitive when the number of experimental samples is even
moderately large. Local Gaussian process approximation (Gramacy and Apley,
2015) was proposed as an accurate and computationally feasible emulation
alternative. However, constructing local sub-designs specific to predictions at
a particular location of interest remains a substantial computational
bottleneck to the technique. In this paper, two computationally efficient
neighborhood search limiting techniques are proposed, a maximum distance method
and a feature approximation method. Two examples demonstrate that the proposed
methods indeed save substantial computation while retaining emulation accuracy.
