Winner-relaxing and winner-enhancing Kohonen maps: Maximal mutual information from enhancing the winner

Jens Christian Claussen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The magnification behavior of a generalized family of self-organizing feature maps, the winner relaxing and winner enhancing Kohonen algorithms is analyzed by the magnification law in the one-dimensional case, which can be obtained analytically. The winner-enhancing case allows to achieve a magnification exponent of one and therefore provides optimal mapping in the sense of information theory. A numerical verification of the magnification law is included, and the ordering behavior is analyzed. Compared to the original self-organizing map and some other approaches, the generalized winner enforcing algorithm requires minimal extra computations per learning step and is conveniently easy to implement.

Original languageEnglish
Pages (from-to)15-22
Number of pages8
JournalComplexity
Volume8
Issue number4
DOIs
Publication statusPublished - 13 Jul 2003

Keywords

  • Kohonen algorithm
  • Magnification exponent
  • Mutual information
  • Self-organizing maps

Fingerprint

Dive into the research topics of 'Winner-relaxing and winner-enhancing Kohonen maps: Maximal mutual information from enhancing the winner'. Together they form a unique fingerprint.

Cite this