Robust Topology Representing Networks

Martinetz and Schulten proposed the use of a Competitive Hebbian Learning (CHL) rule to build Topology Representing Networks. From a set of units and a data distribution, a link is created between the first and second closest units to each datum, creating a graph which preserves the topology of the data set. However, one has to deal with finite data distributions generally corrupted with noise, for which CHL may be unefficient. We propose a more robust approach to create a topology representing graph, by considering the density of the data distribution.

• Type: Conference
• Year: 2003
• Where: ESANN
• Authors: Michaël Aupetit
• Keywords: Competitive Hebbian Learning, Data Distribution, ESANN 2003, ESANN 2007, Topology Representing Networks