ストリーミングガウス・ディリクレ確率場による高次元カテゴリ観測の空間予測
Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observations
時空間的に分布する疎で高次元なカテゴリ観測のストリームを効率的に学習し、有界な時間計算量で推論・クエリを可能にするS-GDRFモデルを提案。プランクトン画像データで変分ガウス過程より高精度な予測を示した。
著者: J. E. San Soucie, H. M. Sosik, Y. Girdhar
分類: cs.RO, cs.LG
原文アブストラクト
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.