Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409136 | Neurocomputing | 2008 | 17 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Pierre Gaillard, Michaël Aupetit, Gérard Govaert,