Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2008 | NIPS | empirical risk minimization,computer graphic,non parametric regression,computer vision,signal processing |
Field | DocType | Citations |
Signal processing,Mathematical optimization,Computer science,Empirical risk minimization,Nonparametric regression,Input/output,Regularization (mathematics),Artificial intelligence,Computer graphics,Manifold,Machine learning,Robotics | Conference | 2 |
PageRank | References | Authors |
0.37 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Florian Steinke | 1 | 269 | 19.19 |
| Matthias Hein | 2 | 663 | 62.80 |