Neighbor Size of Initial Node Exchange and its Influence for SOM Learning
Department of Information and Knowledge Engineering, Tottori University, 4-101 Koyama-cho Minami, Tottori-shi, Tottori 680-8552, Japan
The Self Organizing Map (SOM) involves neural networks, that learns the features of input data through 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 distance that output nodes move increases, slowing learning convergence. We propose solving this problem in initial node exchange using part of learning data. We investigated how the average move distance of all nodes, measured by convergence, changed with differences in the initial size of the neighbor area in node exchange. We clarified the influence of the initial neighbor size on the average move distance of all nodes, clarified the expression of relations, and showed the optimum domain of relations.
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