  We propose a generative model for the spatio-temporal distribution of high
dimensional categorical observations. These are commonly produced by robots
equipped with an imaging sensor such as a camera, paired with an image
classifier, potentially producing observations over thousands of categories.
The proposed approach combines the use of Dirichlet distributions to model
sparse co-occurrence relations between the observed categories using a latent
variable, and Gaussian processes to model the latent variable's spatio-temporal
distribution. Experiments in this paper show that the resulting model is able
to efficiently and accurately approximate the temporal distribution of high
dimensional categorical measurements such as taxonomic observations of
microscopic organisms in the ocean, even in unobserved (held out) locations,
far from other samples. This work's primary motivation is to enable deployment
of informative path planning techniques over high dimensional categorical
fields, which until now have been limited to scalar or low dimensional vector
observations.
