Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations
| Autor: | Yogesh Girdhar, Heidi M. Sosik, John E. San Soucie |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 0209 industrial biotechnology Computer science Gaussian Inference Machine Learning (stat.ML) 02 engineering and technology Latent variable Dirichlet distribution Machine Learning (cs.LG) Computer Science - Robotics symbols.namesake 020901 industrial engineering & automation Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Graphical model Categorical variable Gaussian process Random field business.industry I.2.9 Pattern recognition Generative model symbols 020201 artificial intelligence & image processing Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | ICRA |
| DOI: | 10.1109/icra40945.2020.9196713 |
| Popis: | 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. 8 pages, 10 figures, accepted to proceedings of International Conference on Robotics and Automation (ICRA) 2020 |
| Databáze: | OpenAIRE |
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