Information fusion in multi-task Gaussian processes
This addresses geological resource modeling problems for researchers and practitioners, but it is incremental as it applies an existing method to a new domain.
The paper tackled geological resource modeling by evaluating heterogeneous information fusion with multi-task Gaussian processes, showing that integrating information across sources leads to superior estimates of all modeled quantities compared to individual modeling, with experiments on large-scale real sensor data.
This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compared to modeling them individually. Multi-task Gaussian processes provide a powerful approach for simultaneous modeling of multiple quantities of interest while taking correlations between these quantities into consideration. Experiments are performed on large scale real sensor data.