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Title: Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations
We propose a generative model for the spatiotemporal 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 spatiotemporal 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 the deployment of informative path planning techniques over high dimensional categorical fields, which until now have been limited to scalar or low dimensional vector observations.
Authors:
; ;
Award ID(s):
1734400 1655686
Publication Date:
NSF-PAR ID:
10206411
Journal Name:
2020 IEEE International Conference on Robotics and Automation (ICRA)
Page Range or eLocation-ID:
2924 to 2931
Sponsoring Org:
National Science Foundation
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