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Non-parametric Regression Between Manifolds
Florian Steinke · Matthias Hein

Wed Dec 10 07:30 PM -- 12:00 AM (PST) @

This paper discusses non-parametric regression between Riemannian manifolds. This learning problem arises frequently in many application areas ranging from signal processing, computer vision, over robotics to computer graphics. We present a new algorithmic scheme for the solution of this general learning problem based on regularized empirical risk minimization. The regularization functional takes into account the geometry of input and output manifold, and we show that it implements a prior which is particularly natural. Moreover, we demonstrate that our algorithm performs well in a difficult surface registration problem.

Author Information

Florian Steinke (Max Planck Institute for Biological Cybernetics)
Matthias Hein (University of Tübingen)

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