Learning on Manifolds

In this project the researchers will study some theoretical problems concerning reproducing kernel Hilbert spaces on Riemannian manifolds and consider how the manifold structure affects the efficiency of learning algorithms. The regularity and space structure will be investigated quantitatively. Some kernel based learning algorithms will be studied for the purpose of regression and classification, and learning rates will be derived. The results will provide some connections between learning theory and multivariate approximation.