Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of
  High Dimensional Categorical Observations
J. E. San Soucie H. M. Sosik Y. Girdhar
We present the Streaming Gaussian Dirichlet Random Field (S-GDRF) model, a
novel approach for modeling a stream of spatiotemporally distributed, sparse,
high-dimensional categorical observations. The proposed approach efficiently
learns global and local patterns in spatiotemporal data, allowing for fast
inference and querying with a bounded time complexity. Using a high-resolution
data series of plankton images classified with a neural network, we demonstrate
the ability of the approach to make more accurate predictions compared to a
Variational Gaussian Process (VGP), and to learn a predictive distribution of
observations from streaming categorical data. S-GDRFs open the door to enabling
efficient informative path planning over high-dimensional categorical
observations, which until now has not been feasible.