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Free energy-based competitive learning for self-organizing maps. (English) Zbl 1157.68424
Gammerman, A. (ed.), Artificial intelligence and applications. Machine learning. As part of the 26th IASTED international multi-conference on applied informatics. Calgary: International Association of Science and Technology for Development (IASTED); Anaheim, CA: Acta Press (ISBN 978-0-88986-710-9/CD-ROM). 414-419 (2008).
Summary: In this paper, we propose a new information-theoretic approach to self-organizing maps. We have so far proposed mutual information maximization to realize competitive learning. However, the computational complexity and fidelity to input patterns become serious when we try to apply it to self-organizing maps. To overcome these shortcomings, we introduce a free energy similar to that of statistical mechanics. By the free energy, we need not directly compute mutual information to simplify greatly computational procedures. In addition, in the free energy, errors between targets and outputs are naturally built in. This property can solve the problem of fidelity to input patterns of mutual information maximization. In the free energy, we can increase mutual information, taking due attention to errors between targets and connection weights. To demonstrate the performance of our free energy method, we applied the method to the famous Iris problem. Experimental results showed that feature maps obtained by free energy minimization was significantly similar to those by the conventional SOM.
For the entire collection see [Zbl 1154.68012].

MSC:
68T05 Learning and adaptive systems in artificial intelligence
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