A growing neural gas network learns topologies

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

Overview

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.