Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations
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DOI：
10.1109/icra40945.2020.9196713
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
J. E. S. Soucie;H. Sosik;Yogesh A. Girdhar
中科院分区：
文献类型：
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作者：
J. E. S. Soucie;H. Sosik;Yogesh A. Girdhar
我们提出了一个高维分类观测时空分布的生成模型。这些通常是由配备成像传感器（如相机）的机器人产生的，与图像分类器配对，可能产生数千个类别的观察结果。该方法结合了使用Dirichlet分布来模拟观测类别之间使用潜在变量的稀疏共现关系，以及使用高斯过程来模拟潜在变量的时空分布。本文中的实验表明，所得到的模型能够有效而准确地近似高维分类测量的时间分布，例如海洋中微生物的分类观察，即使是在远离其他样本的未观察（保留）位置。这项工作的主要动机是能够在高维分类场上部署信息路径规划技术，到目前为止，这些技术仅限于标量或低维矢量观测。
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.