Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observations

arXiv:2402.15359v1 Announce Type: cross
Abstract: 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 …

abstract arxiv categorical cs.lg cs.ro data distributed fields global inference modeling novel patterns predictions random spatial streaming type