Inferring manifolds using Gaussian processes

Understand

It is often of interest to infer lower-dimensional structure underlying complex data.

• As a flexible class of non-linear structures, it is common to focus on Riemannian manifolds.
• Most existing manifold learning algorithms replace the original data with lower-dimensional coordinates without providing an estimate of the manifold or using the manifold to denoise the original data.
• This article proposes a new methodology to address these problems, allowing interpolation of the estimated manifold between the fitted data points.