JGPR: a computationally efficient multi-target Gaussian process regression algorithm

Multi-target regression algorithms are designed to predict multiple outputs at the same time, and allow us to take all output variables into account during the training phase. Despite the recent advances, this context of machine learning is still an open challenge for developing a low-cost and high accurate algorithm. The main challenge in multi-target regression algorithms is how to use different targets’ information in the training and/or test phases. In this paper, we introduce a low-cost multi-target Gaussian process regression (GPR) algorithm, called joint GPR (JGPR) that employs a shared covariance matrix among the targets during the training phase and solves a sub-optimal cost function for optimization of hyperparameters. The proposed strategy reduces the computational complexity considerably during the training and test phases and simultaneously avoids overfitting of the multi-target regression algorithm upon the targets. We have performed extensive experiments on both simulated data and 18 benchmark datasets to assess the proposed method compared with other multi-target regression algorithms. Experimental results show that the proposed JGPR outperforms the state-of-the-art approaches on most of the given benchmark datasets.

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10994-022-06170-3