Double Competition for Information-Theoretic SOM

In this paper, we propose a new type of informationtheoretic method for the self-organizing maps (SOM), taking into account competition between competitive (output) neurons as well as input neurons. The method is called ”double competition”, as it considers competition between outputs as well as input neurons. By increasing information in input neurons, we expect to obtain more detailed information on input patterns through the information-theoretic method. We applied the informationtheoretic methods to two well-known data sets from the machine learning database, namely, the glass and dermatology data sets. We found that the information-theoretic method with double competition explicitly separated the different classes. On the other hand, without considering input neurons, class boundaries could not be explicitly identified. In addition, without considering input neurons, quantization and topographic errors were inversely related. This means that when the quantization errors decreased, topographic errors inversely increased. However, with double competition, this inverse relation between quantization and topographic errors was neutralized. Experimental results show that by incorporating information in input neurons, class structure could be clearly identified without degrading the map quality to severely.

@article{Kamimura2014,
  title     = {Double Competition for Information-Theoretic SOM},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {3},
  number    = {11},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Ryotaro Kamimura},
  doi       = {10.14569/IJARAI.2014.031104},
  url       = {https://doi.org/10.14569/IJARAI.2014.031104}
}