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An information-theoretic approach to feature extraction in competitive learning. (English) Zbl 1157.68425
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). 388-394 (2008).
Summary: In this paper, we propose a new information-theoretic approach to competitive learning and self-organizing maps. We use several information-theoretic measures such as conditional information and information losses to extract main features in input patterns. For each competitive unit, conditional information content is used to show how much information on input patterns is contained. In addition, for detecting the importance of each variable, information losses are introduced. The information loss is defined by difference between information with all input units and information without an input unit. We applied the method to an artificial data, the Iris problem and a student survey. In all cases, experimental results showed that main features in input patterns were clearly detected.
For the entire collection see [Zbl 1154.68012].
MSC:
68T05 Learning and adaptive systems in artificial intelligence
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