A parallel approach to plastic neural gas

A parallel implementation of unsupervised vector-quantization networks can reduce the high computational load of the training process. First, a plastic version of the neural gas algorithm is presented. Then, the paper describes how a toroidal mesh topology fits the neural model for a distributed implementation. The architecture adopted and the data-allocation strategy enhance the method´s scaling properties and remarkable efficiency. Experimental results on a significant testbed (low bit-rate image compression) confirm the validity of the parallel approach