Efficient spatio-temporal Gaussian regression via Kalman filtering
نویسندگان
چکیده
منابع مشابه
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data. GPs have been mainly applied to spatial regression where they represent one of the most powerful estimation approaches also thanks to their universal representing properties. Their extension to dynamical processes has been instead elusive so...
متن کاملInfinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression
We show how spatio-temporal Gaussian process (GP) regression problems (or the equivalent Kriging problems) can be formulated as infinite-dimensional Kalman filtering and Rauch-Tung-Striebel (RTS) smoothing problems, and present a procedure for converting spatio-temporal covariance functions into infinite-dimensional stochastic differential equations (SDEs). The resulting infinitedimensional SDE...
متن کاملKalman Filtering Motion Prediction Forrecursive Spatio - Temporal Segmentation
In the framework of computer vision, the spatio-temporal se-gmentation procedure plays a central role. It aims at identifying in the input image, semantically meaningful features that are relevant for the problem at hand. In this paper, these features are selected to be the objects forming the scene. The objects are deened by their properties of temporal and spatial coherence through the video ...
متن کاملInference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering
Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations,...
متن کاملEfficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling
This paper presents an efficient Gaussian process inference scheme for modeling shortscale phenomena in spatio-temporal datasets. Our model uses a sum of separable, compactly supported covariance functions, which yields a full covariance matrix represented in terms of small sparse matrices operating either on the spatial or temporal domain. The proposed inference procedure is based on Gibbs sam...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Automatica
سال: 2020
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2020.109032