A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes

2 code implementations • 8 Aug 2020 • Raphael Gautier, Piyush Pandita, Sayan Ghosh, Dimitri Mavris

The comparison shows that the proposed method improves the active subspace recovery and predictive accuracy, in both the deterministic and probabilistic sense, when only few model observations are available for training, at the cost of increased training time.

Dimensionality Reduction Gaussian Processes