Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map

Details Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map

2006-09-20

arxiv.org/abs/cond-mat/0609509v1

Lecture Notes in Computer Science 2415, 939-944 (2002)

Physics

Condensed Matter

Disordered Systems and Neural Networks

8 pages, 10 figures. Link to publisher under http://link.springer.de/link/service/series/0558/bibs/2415/24150939.htm

Scientific paper

Whileas the Kohonen Self Organizing Map shows an asymptotic level density following a power law with a magnification exponent 2/3, it would be desired to have an exponent 1 in order to provide optimal mapping in the sense of information theory. In this paper, we study analytically and numerically the magnification behaviour of the Elastic Net algorithm as a model for self-organizing feature maps. In contrast to the Kohonen map the Elastic Net shows no power law, but for onedimensional maps nevertheless the density follows an universal magnification law, i.e. depends on the local stimulus density only and is independent on position and decouples from the stimulus density at other positions.

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