Representation-burden conservation network applied to learning VQ

Abstract

A self-creating network effective in learning vector quantization, called RCN (Representation-burden Conservation Network) is developed. Each neuron in RCN is characterized by a measure of representation-burden. Conservation is achieved by bounding the summed representation-burden of all neurons at constant 1, as representation-burden values of all neurons are updated after each input presentation. We show that RCN effectively fulfills the conscience principle [1] and achieves biologically plausible self-development capability. In addition, conservation in representation-burden facilitates systematic derivations of learning parameters, including the adaptive learning rate control useful in accelerating the convergence as well as in improving node-utilization. Because it is smooth and incremental, RCN can overcome the stability-plasticity dilemma. Simulation results show that RCN displays superior performance over other competitive learning networks in minimizing the quantization error.