Self-Organizing Map Learning with Momentum

Self-organizing map (SOM) is a type of artificial neural network for cluster analysis. Each neuron in the map competes with others for the input data objects in order to learn the grouping of the input space. Besides competition, neighbor neurons of a winning neuron also learn. SOM has a natural propensity to cluster data into visually distinct clusters, which show the intrinsic grouping of data. The self-organizing map algorithm is heuristic in nature and will almost always converge. Since self-organizing map may be trapped in a local optimum, so we introduce momentum into the learning process thus the movement of a neuron may jump over local optimum. We expect this will be similar to the learning of neurons in back-propagation with momentum. Like the learning process in back-propagation, the timing for updating the amount of movement of a neuron is either batch mode or incremental mode. However, due to the neighborhood function, the movement of a non-winner neuron is relatively small as compare to when it is a winner. So when deciding the momentum, the previous movement of a neuron needs special consideration. Experiment result show that adding momentum to self-organizing map considerably contributes to the acceleration of the convergence.