Name
Abstract | ||
|---|---|---|
We deal with the nonlinear manifold learning problem to find a low-dimensional structure in high-dimensional data. Based on Gaussian random fields framework, we propose an approximate sampling method for coordinates on the manifolds. Experimentally the mean of samples are shown to be almost equal to the coordinates obtained by locally linear embedding where the generated set of samples of coordinates show interesting variety. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IJCNN.2007.4371023 | IJCNN |
Keywords | Field | DocType |
gaussian random fields framework,approximation theory,learning (artificial intelligence),nonlinear manifold learning problem,locally linear embedding system,gaussian processes,approximate sampling method,sampling methods,embedded systems,high dimensional data,learning artificial intelligence,manifold learning,gaussian random field | Embedding,Random field,Action-angle coordinates,Gaussian process,Artificial intelligence,Sampling (statistics),Orthogonal coordinates,Generalized coordinates,Machine learning,Mathematics,Manifold | Conference |
ISSN | ISBN | Citations |
1098-7576 E-ISBN : 978-1-4244-1380-5 | 978-1-4244-1380-5 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyun Chul Kim | 1 | 65 | 5.82 |
| Kyu-Hwan Jung | 2 | 82 | 4.82 |
| Jaewook Lee | 3 | 72 | 8.87 |