Fast competitive learning algorithm for image compression neural networks
A fast training algorithm for competitive learning neural networks is presented. The algorithm identifies the full Euclidean distance calculation as the major bottleneck. Through theoretical analysis, a simple approximate distance is derived and used as the pre-test to exclude most of the neurons in competitive learning. Thus provides significant efficiency improvement over the standard algorithm.
