Ordering of Self-Organizing Maps in Multidimensional Cases

Ordering of Self-Organizing Maps in Multidimensional Cases

It has been proved that in one-dimensional cases, the weights of Kohonen's self-organizing maps (SOM) will become ordered with probability 1; once the weights are ordered, they cannot become disordered in future training. It is difficult to analyze Kohonen's SOMs in multidimensional cases;...

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Veröffentlicht in:Neural computation 1998-01, Vol.10 (1), p.19-23
Hauptverfasser: Huang, Guang-Bin, Babri, Haroon A., Li, Hua-Tian
Format: Artikel
Sprache:eng
Schlagworte:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Pattern recognition. Digital image processing. Computational geometry
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Zusammenfassung:It has been proved that in one-dimensional cases, the weights of Kohonen's self-organizing maps (SOM) will become ordered with probability 1; once the weights are ordered, they cannot become disordered in future training. It is difficult to analyze Kohonen's SOMs in multidimensional cases; however, it has been conjectured that similar results seem to be obtainable in multidimensional cases. In this note, we show that in multidimensional cases, even though the weights are ordered at some time, it is possible that they become disordered in the future.
ISSN:0899-7667
1530-888X
DOI:10.1162/089976698300017872