Self-organizing feature maps with self-adjusting learning parameters

This paper presents an extension of the self-organizing learning algorithm of feature maps in order to improve its convergence to neighborhood preserving maps. The Kohonen learning algorithm is controlled by two learning parameters, which have to be chosen empirically because there exists neither rules nor a method for their calculation. Consequently, often time consuming parameter studies have to precede before a neighborhood preserving feature map is obtained. To circumvent those lengthy numerical studies, here, a method is presented and incorporated into the learning algorithm which determines the learning parameters automatically. Therefore, system models of the learning and organizing process are developed in order to be followed and predicted by linear and extended Kalman filters. The learning parameters are optimal within the system models, so that the self-organizing process converges automatically to a neighborhood preserving feature map of the learning data.