Self-Organizing Maps
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Information-Theoretic Competitive Learning with Inverse Euclidean Distance Output Units
Neural Processing Letters
Information Discriminant Analysis: Feature Extraction with an Information-Theoretic Objective
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
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.