Gaussian process regression within an active learning scheme

In this work, we face the problem of training sample collection for the estimation of biophysical parameters by adopting the active learning approach. In particular, we propose two active learning strategies specifically developed for Gaussian Process (GP) regression. The first one is based on adding samples that are distant from the current training samples in the kernel space while the second one exploits an intrinsic GP regression outcome to pick up the most difficult samples. Experiments on simulated and real data sets show the effectiveness of active selection of training samples for regression problems.