Rival-model penalized self-organizing map

Yiu Ming CHEUNG*, Lap Tak Law

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

42 Citations (Scopus)


As a typical data visualization technique, self-organizing map (SOM) has been extensively applied to data clustering, image analysis, dimension reduction, and so forth. In a conventional adaptive SOM, it needs to choose an appropriate learning rate whose value is monotonically reduced over time to ensure the convergence of the map, meanwhile being kept large enough so that the map is able to gradually learn the data topology. Otherwise, the SOM's performance may seriously deteriorate. In general, it is nontrivial to choose an appropriate monotonically decreasing function for such a learning rate. In this letter, we therefore propose a novel rival-model penalized self-organizing map (RPSOM) learning algorithm that, for each input, adaptively chooses several rivals of the best-matching unit (BMU) and penalizes their associated models, i.e., those parametric real vectors with the same dimension as the input vectors, a little far away from the input. Compared to the existing methods, this RPSOM utilizes a constant learning rate to circumvent the awkward selection of a monotonically decreased function for the learning rate, but still reaches a robust result. The numerical experiments have shown the efficacy of our algorithm.

Original languageEnglish
Pages (from-to)289-295
Number of pages7
JournalIEEE Transactions on Neural Networks
Issue number1
Publication statusPublished - Jan 2007

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Constant learning rate
  • Rival-model penalized self-organizing map (RPSOM)
  • Self-organizing map (SOM)


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