  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.
