Optimizing the self-organizing-process of topology maps

Abstract

This contribution proposes, how the self-organizing process of feature maps can be improved.

The self-organizing process converges to a map, which preserves the neighbourhood relations of the input data, if the learning parameters, learning coefficient and width of the neighbourhood function, are chosen correctly. In general, the parameters are chosen empirically, dependent on the distribution of the training data and the network architecture [3]. Consequently, some experience with the algorithm and the training data is needed to choose proper courses of learning parameters. To avoid time consuming parameter studies a system model of the self-organizing process is developed and a linear Kalman filter used to estimate the learning coefficient. To estimate the width of the neighbourhood function the process of neighbourhood preservation during the training is modelled for the first time successfully. This process is then followed by an extended Kalman filter algorithm, which estimates the width of the neighbourhood function.

In case of fast self-organizing algorithms, as published in [1], the proposed parameter estimation method is essential for the training of data with unknown density distribution.