Abstract
In this paper, we propose a new type of information-theoretic method for competitive learning based, upon mutual information between competitive units and input patterns. In addition, we extend this method to a case where cooperation between competitive units exists to realize self-organizing maps. In computational methods, free energy is introduced to simplify the computation of mutual information. We applied our method to two problems, namely, the Senate data and ionosphere data problems. In both, experimental results confirmed that better performance could be obtained in terms of quantization and topographic errors. We also found that the information-theoretic methods tended to produce more equi-probable distribution of competitive units.
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Kamimura, R. (2010). Information-Theoretic Competitive and Cooperative Learning for Self-Organizing Maps. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_52
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DOI: https://doi.org/10.1007/978-3-642-17534-3_52
Publisher Name: Springer, Berlin, Heidelberg
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