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.

翻译：我们提出了流式高斯狄利克雷随机场（S-GDRF）模型，这是一种用于建模时空分布、稀疏且高维分类观测数据流的新方法。该方法能够高效学习时空数据中的全局与局部模式，在有限时间复杂度内实现快速推断与查询。通过利用神经网络分类的高分辨率浮游生物图像序列数据，我们证明了该方法相比变分高斯过程（VGP）能做出更准确的预测，并能从流式分类数据中学习观测的预测性分布。S-GDRF模型为在高维分类观测数据上实现高效的信息性路径规划开辟了可能，而这一目标此前一直无法实现。