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Ridge functions have recently emerged as a powerful set of ideas for subspace-based dimension reduction. In this paper we begin by drawing parallels between ridge subspaces, sufficient dimension reduction and active subspaces; contrasting between techniques rooted in statistical regression to those rooted in approximation theory. This sets the stage for our new algorithm that approximates what we call a Gaussian ridge function---the posterior mean of a Gaussian process on a dimension reducing subspace---suitable for both regression and approximation problems. To compute this subspace we develop an iterative algorithm that optimizes over the Grassmann manifold to compute the subspace, followed by an optimization of the hyperparameters of the Gaussian process. We demonstrate the utility of the algorithm on an analytical function, where we obtain near exact ridge recovery, and a turbomachinery case study, where we compare the efficacy of our approach with four well known sufficient dimension reduction methods: MAVE, SIR, SAVE, CR. The comparisons motivate the use of the posterior variance as a heuristic for identifying the suitability of a dimension reducing subspace.

Dimension Reduction via Gaussian Ridge Functions