Open AccessProceedings Article
A Growing Neural Gas Network Learns Topologies
Bernd Fritzke
- Vol. 7, pp 625-632
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TLDR
An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule.Abstract:
An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994), this model has no parameters which change over time and is able to continue learning, adding units and connections, until a performance criterion has been met. Applications of the model include vector quantization, clustering, and interpolation.read more
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References
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TL;DR: A new self-organizing neural network model that has two variants that performs unsupervised learning and can be used for data visualization, clustering, and vector quantization is presented and results on the two-spirals benchmark and a vowel classification problem are presented that are better than any results previously published.
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Topology representing networks
TL;DR: This competitive Hebbian rule provides a novel approach to the problem of constructing topology preserving feature maps and representing intricately structured manifolds and makes this novel approach particularly useful in all applications where neighborhood relations have to be exploited or the shape and topology of submanifolds have to been take into account.
Book ChapterDOI
Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps
TL;DR: In this article, it was shown that Hebbian learning with competition leads to lateral connections that correspond to the edges of the induced Delaunay triangulation and leads to a network structure that forms a topology preserving map of a given manifold, independent of the manifold's topology.