Name
Abstract | ||
|---|---|---|
Sparse Gaussian process (GP) models provide an efficient way to perform regression on large data sets. The key idea is to select a representative subset of the available training data, which induces the sparse GP model approximation. In the past, a variety of selection criteria for GP approximation have been proposed, but they either lack accuracy or suffer from high computational costs. In this paper, we introduce a novel and straightforward criterion for successive selection of training points used for GP model approximation. The proposed algorithm allows a fast and efficient selection of training points, while being competitive in learning performance. As evaluation, we employ our approach in learning inverse dynamics models for robot control using very large data sets (e.g. 500.000 samples). It is demonstrated in experiments that our approximated GP model is sufficiently fast for real-time prediction in robot control. Comparisons with other state-of-the-art approximation techniques show that our proposed approach is significantly faster, while being competitive to generalization accuracy. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICRA.2015.7139547 | IEEE International Conference on Robotics and Automation |
Keywords | Field | DocType |
Gaussian processes,approximation theory,learning (artificial intelligence),regression analysis,robot dynamics,compliant real-time robot control,large data sets,learning inverse dynamics models,learning performance,sparse GP model approximation,sparse Gaussian process regression,training point selection | Kriging,Data modeling,Robot control,Data set,Computer science,Sparse approximation,Artificial intelligence,Gaussian process,Inverse dynamics,Robot,Machine learning | Conference |
Volume | Issue | ISSN |
2015 | 1 | 1050-4729 |
Citations | PageRank | References |
4 | 0.48 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jens Schreiter | 1 | 21 | 1.83 |
| Peter Englert | 2 | 21 | 3.41 |
| duy nguyentuong | 3 | 438 | 26.22 |
| marc toussaint | 4 | 1299 | 97.23 |