Name
Abstract | ||
|---|---|---|
We consider the problem of denoising a noisily sampled submanifold M in Rd, where the submanifoldM is a priori unknown and we are only given a noisy point sample. The presented denoising algorithm is based on a graph-based diffusion process of the point sample. We analyze this diffusion process using recent re- sults about the convergence of graph Laplacians. In the experiments we show that our method is capable of dealing with non-trivial high-dimensional noise. More- over using the denoising algorithm as pre-processing method we can improve the results of a semi-supervised learning algorithm. |
Year | Venue | DocType |
|---|---|---|
2006 | NIPS | Conference |
Citations | PageRank | References |
10 | 1.13 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Hein | 1 | 663 | 62.80 |
| Markus Maier | 2 | 91 | 7.26 |