Name
Abstract | ||
|---|---|---|
The generative topographic mapping (GTM) models data by a mixture of Gaussians induced by a low-dimensional lattice of latent points in low dimensional space. Using back-projection, topographic mapping and visualization can be achieved. The original GTM has been proposed for vectorial data only and, thus, cannot directly be used to visualize data given by pairwise dissimilarities only. In this contribution, we consider an extension of GTM to dissimilarity data. The method can be seen as a direct pendant to GTM if the dissimilarity matrix can be embedded in Euclidean space while constituting a model in pseudo-Euclidean space, otherwise. We compare this visualization method to recent alternative visualization tools. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16111-7_26 | KI |
Keywords | Field | DocType |
original gtm,recent alternative visualization tool,generative topographic mapping,vectorial data,visualization method,dissimilarity data,models data,dissimilarity matrix,pseudo-euclidean space,low dimensional space,euclidean space,topographic map,mixture of gaussians | Computer vision,Pairwise comparison,Generative topographic map,Pattern recognition,Visualization,Topographic map,Matrix (mathematics),Euclidean space,Artificial intelligence,Generative topographic mapping,Mathematics,Mixture model | Conference |
Volume | ISSN | ISBN |
6359 | 0302-9743 | 3-642-16110-3 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrej Gisbrecht | 1 | 195 | 15.60 |
| Bassam Mokbel | 2 | 189 | 14.73 |
| Alexander Hasenfuss | 3 | 274 | 14.56 |
| Barbara Hammer | 4 | 2383 | 181.34 |