A scale-invariant feature map
Colin Fyfe · Network Computation in Neural Systems · 1996
We use a simple network which uses negative feedback of activation and simple Hebbian learning to self-organize in such a way as to produce a hierarchical classification network. By adding neighbourhood relations to its learning rule, we create a feature map which has the property of retaining the angular properties of the input data, i.e. vectors of similar directions are classified similarly regardless of their magnitude. We use neither renormalization of weights nor data preprocessing in the network despite using competition based on maximizing the neuron's activation. * The paper was presented at the Workshop on Information Theory and the Brain, held at the University of Stirling, UK, on 4–5 September 1995.