A sparse covariance function for exact Gaussian process inference in large datasets

Authors:
Arman Melkumyan;Fabio Ramos
Affiliations:
Australian Centre for Field Robotics, The University of Sydney, NSW, Australia;Australian Centre for Field Robotics, The University of Sydney, NSW, Australia
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

Citing 2
Cited 1

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression

The Journal of Machine Learning Research

Multi-kernel Gaussian processes

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two

Quantified Score

Hi-index	0.03

Visualization

Abstract

Despite the success of Gaussian processes (GPs) in modelling spatial stochastic processes, dealing with large datasets is still challenging. The problem arises by the need to invert a potentially large covariance matrix during inference. In this paper we address the complexity problem by constructing a new stationary covariance function (Mercer kernel) that naturally provides a sparse covariance matrix. The sparseness of the matrix is defined by hyperparameters optimised during learning. The new covariance function enables exact GP inference and performs comparatively to the squared-exponential one, at a lower computational cost. This allows the application of GPs to large-scale problems such as ore grade prediction in mining or 3D surface modelling. Experiments show that using the proposed covariance function, very sparse covariance matrices are normally obtained which can be effectively used for faster inference and less memory usage.