Relation Organization of SOM Initial Map by Improved Node Exchange

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The Self Organizing Map (SOM) involves neural networks, that learns the features of input data thorough unsupervised, competitive neighborhood learning. In the SOM learning algorithm, connection weights in a SOM feature map are initialized at random values, which also sets nodes at random locations in the feature map independent of input data space. The move distance of output nodes increases, slowing learning convergence. As precedence research, we proposed the method to improve this problem, initial node exchange by using a part of feature map. In this paper, we propose two improved exchange method, node exchange with fixed neighbor area and spiral node exchange. The node exchange with fixed neighbor area uses fixed position of winner node and fixed initial size of neighbor area that sets to cover whole feature map. We investigate how average move distance of all nodes and average deviation of move distance would change with the differences by type of fixed neighbor area in node exchange process. The spiral node exchange is used instead of neighbor area reduction reputation of former method. By spiral node exchange, repetition by node exchange process becomes needless and can expect speed up of total processing.