A stochastic competitive learning algorithm
Abdesselam Bouzerdoum · 2002
We introduce a new stochastic competitive learning algorithm (SCoLA). Here the criterion for selecting the winning neuron consists of a deterministic component and a stochastic component. The deterministic component is inversely proportional to the distance between the input vector and the weight vector, whereas the stochastic component is a zero-mean normal random variable whose variance decreases monotonically with the frequency of winning the competition. Neurons that do not frequently win have high variance, and thus a better chance of winning the competition. Simulation results are presented which demonstrate the effectiveness of the proposed stochastic competitive learning scheme. It achieves better neuron utilization than conventional competitive learning does, resulting in lower distortion rates in clustering and vector quantization applications.