A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Computation with infinite neural networks
Neural Computation
Regression with input-dependent noise: a Gaussian process treatment
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Gaussian Processes for Model Fusion
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Heteroscedastic Gaussian process regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Most likely heteroscedastic Gaussian process regression
Proceedings of the 24th international conference on Machine learning
A Bayesian regression approach to terrain mapping and an application to legged robot locomotion
Journal of Field Robotics - Three-Dimensional Mapping, Part 1
Gaussian process modeling of large-scale terrain
Journal of Field Robotics - Three-Dimensional Mapping, Part 1
Some new results on neural network approximation
Neural Networks
Transformations of gaussian process priors
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Multi-kernel Gaussian processes
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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This paper addresses the problem of fusing multiple sets of heterogeneous sensor data using Gaussian processes (GPs). Experiments on large scale terrain modeling in mining automation are presented. Three techniques in increasing order of model complexity are discussed. The first is based on adding data to an existing GP model. The second approach treats data from different sources as different noisy samples of a common underlying terrain and fusion is performed using heteroscedastic GPs. The final approach, based on dependent GPs, models each data set by a separate GP and learns spatial correlations between data sets through auto and cross covariances. The paper presents a unifying view of approaches to data fusion using GPs, a statistical evaluation that compares these approaches and multiple previously untested variants of them and an insight into the effect of model complexity on data fusion. Experiments suggest that in situations where data being fused is not rich enough to require a complex GP data fusion model or when computational resources are limited, the use of simpler GP data fusion techniques, which are constrained versions of the more generic models, reduces optimization complexity and consequently can enable superior learning of hyperparameters, resulting in a performance gain.