Name
Title | ||
|---|---|---|
Learning topology of a labeled data set with the supervised generative Gaussian graph |
Abstract | ||
|---|---|---|
Extracting the topology of a set of a labeled data is expected to provide important information in order to analyze the data or to design a better decision system. In this work, we propose to extend the generative Gaussian graph to supervised learning in order to extract the topology of labeled data sets. The graph obtained learns the intra-class and inter-class connectedness and also the manifold-overlapping of the different classes. We propose a way to vizualize these topological features. We apply it to analyze the well-known Iris database and the three-phase pipe flow database. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.neucom.2007.12.028 | Neurocomputing |
Keywords | DocType | Volume |
delaunay graph,inter-class connectedness,topological feature,supervised topology learning,topology representing graph,supervised generative gaussian graph,better decision system,three-phase pipe flow database,gabriel graph,well-known iris database,mixture models,different class,supervised learning,generative gaussian graph,em algorithm,important information | Conference | 71 |
Issue | ISSN | Citations |
7-9 | Neurocomputing | 12 |
PageRank | References | Authors |
0.64 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre Gaillard | 1 | 79 | 10.89 |
| Michaël Aupetit | 2 | 261 | 25.59 |
| Gérard Govaert | 3 | 867 | 117.33 |