Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observations
<|reference_start|>Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observations: 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.<|reference_end|>
@article{soucie2024streaming,
    title={Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of
  High Dimensional Categorical Observations},
    author={J. E. San Soucie, H. M. Sosik, Y. Girdhar},
    journal={arXiv preprint arXiv:2402.15359},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15359},
    primaryClass={cs.RO cs.LG}
}