Gaussian Process Landmarking on Manifolds

TL;DR

This paper introduces a Gaussian process-based algorithm for sampling manifolds that improves biological shape analysis by selecting points with maximum uncertainty, with theoretical guarantees on prediction error decay.

Contribution

It presents a novel sequential sampling algorithm on manifolds with proven error bounds, linking Gaussian processes to reduced basis methods for the first time.

Findings

Outperforms user-placed landmarks in biological shape representation

Provides an upper bound on mean squared prediction error

Achieves decay rates comparable to optimal design methods

Abstract

As a means of improving analysis of biological shapes, we propose an algorithm for sampling a Riemannian manifold by sequentially selecting points with maximum uncertainty under a Gaussian process model. This greedy strategy is known to be near-optimal in the experimental design literature, and appears to outperform the use of user-placed landmarks in representing the geometry of biological objects in our application. In the noiseless regime, we establish an upper bound for the mean squared prediction error (MSPE) in terms of the number of samples and geometric quantities of the manifold, demonstrating that the MSPE for our proposed sequential design decays at a rate comparable to the oracle rate achievable by any sequential or non-sequential optimal design; to our knowledge this is the first result of this type for sequential experimental design. The key is to link the greedy algorithm to reduced basis methods in the context of model reduction for partial differential equations. We expect this approach will find additional applications in other fields of research.