Fragmentization of distance measure for pattern generation by a self-organizing map

A self-organizing map (SOM) can be seen as an analytical tool to discover some underlying rules in the given data set. Based on such distinctive nature called topology-preserving projection, a new method for generating intermediate patterns was proposed. According to the results of preceding studies, most developed patterns are not morphing but dissolve. Then, in order to overcome this problem, a fragmentized distance measure is introduced in this paper. As a result of computer simulations, it is confirmed that some asymmetrical patterns are developed even though only symmetrical ones are used for training. This fact reminds us that the distance measure is quite essential, because a feature map is developed through training based on the distance measure.