  The next generation of large scale imaging surveys (such as those conducted
with the Large Synoptic Survey Telescope and Euclid) will require accurate
photometric redshifts in order to optimally extract cosmological information.
Gaussian Processes for photometric redshift estimation (GPz) is a promising new
method that has been proven to provide efficient, accurate photometric redshift
estimations with reliable variance predictions. In this paper, we investigate a
number of methods for improving the photometric redshift estimations obtained
using GPz (but which are also applicable to others). We use spectroscopy from
the Galaxy and Mass Assembly Data Release 2 with a limiting magnitude of r<19.4
along with corresponding Sloan Digital Sky Survey visible (ugriz) photometry
and the UKIRT Infrared Deep Sky Survey Large Area Survey near-IR (YJHK)
photometry. We evaluate the effects of adding near-IR magnitudes and angular
size as features for the training, validation and testing of GPz and find that
these improve the accuracy of the results by ~15-20 per cent. In addition, we
explore a post-processing method of shifting the probability distributions of
the estimated redshifts based on their Quantile-Quantile plots and find that it
improves the bias by ~40 per cent. Finally, we investigate the effects of using
more precise photometry obtained from the Hyper Suprime-Cam Subaru Strategic
Program Data Release 1 and find that it produces significant improvements in
accuracy, similar to the effect of including additional features.
