Competitive learning

Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of the input data.^[1] A variant of Hebbian learning, competitive learning works by increasing the specialization of each node in the network. It is well suited to finding clusters within data.

Models and algorithms based on the principle of competitive learning include vector quantization and self-organising maps (Kohonen maps).

Example algorithm

Here is a simple competitive learning algorithm to find three clusters within some input data.

1. (Set-up.) Let a set of sensors all feed into three different nodes, so that every node is connected to every sensor. Let the weights that each node gives to its sensors be set randomly between 0.0 and 1.0. Let the output of each node be the sum of all its sensors, each sensor's signal strength being multiplied by its weight.

2. When the net is shown an input, the node with the highest output is deemed the winner. The input is classified as being within the cluster corresponding to that node.

3. The winner updates each of its weights, moving weight from the connections that gave it weaker signals to the connections that gave it stronger signals.

Thus, as more and more data is received, each node "listens" more and more carefully to the sensors that relate to one cluster, and "listens" less and less to sensors that relate to other clusters.

References

1. ^ Rumelhart, David; David Zipser, James L. McClelland, et al. (1986). Parallel Distributed Processing, Vol. 1. MIT Press. pp. 151–193.