A growing neural gas network learns topologies

A growing neural gas network learns topologies by Bernd Fritzke (1995).


Hebbian learning variations for learning topologies in high-dimensional data.

Key concepts

  • topological learning: Given some high-dimensional data distribution, find a topological structure which closely reflects the topology of the data distribution.

  • competitive Hebbian learning (CHL): For each input signal x connect the two closest centers (measured by Euclidean distance) by an edge.

  • vector quantization (VC): Often used for data compression, quantization involves dividing a large set of points (vectors) into groups having approximatley the same number of points closest to them. Each group is representated by its centroid (similar to k-means).

  • competitive learning: A type of ANN where nodes compete for the right to respond to a subset of input data.

  • neural gas: For each input signal x adapt the k nearest centers whereby k is decreasing from a large initial to a small final value.