Name
Abstract | ||
|---|---|---|
We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient representations of grasping data for two robotic scenarios. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICRA.2013.6630707 | 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) |
Keywords | Field | DocType |
optimization,data reduction,learning artificial intelligence,data models,humanoid robots,data structures,kernel,robots,encoding,shape | Data structure,Automatic summarization,Dimensionality reduction,Pattern recognition,Computer science,Artificial intelligence,Probabilistic logic,Cluster analysis,Machine learning,Humanoid robot,Encoding (memory),Bayesian probability | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1050-4729 |
Citations | PageRank | References |
2 | 0.39 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Hjelm | 1 | 7 | 1.51 |
| carl henrik ek | 2 | 327 | 30.76 |
| Renaud Detry | 3 | 183 | 13.94 |
| hedvig kjellstrom | 4 | 491 | 42.24 |
| Danica Kragic | 5 | 2070 | 142.17 |