Magnification control in self-organizing maps and neural gas

Abstract

We consider different ways to control the magnification in self-organizing maps (SOM) and neural gas (NG). Starting from early approaches of magnification control in vector quantization, we then concentrate on different approaches for SOM and NG. We show that three structurally similar approaches can be applied to both algorithms that are localized learning, concave-convex learning, and winner-relaxing learning. Thereby, the approach of concave-convex learning in SOM is extended to a more general description, whereas the concave-convex learning for NG is new. In general, the control mechanisms generate only slightly different behavior comparing both neural algorithms. However, we emphasize that the NG results are valid for any data dimension, whereas in the SOM case, the results hold only for the one-dimensional case.

Publication DOI: https://doi.org/10.1162/089976606775093918
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: © 2005 Massachusetts Institute of Technology
Uncontrolled Keywords: Arts and Humanities (miscellaneous),Cognitive Neuroscience
Publication ISSN: 1530-888X
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.mit ... 976606775093918 (Publisher URL)
PURE Output Type: Article
Published Date: 2006-02-01
Authors: Villmann, Thomas
Claussen, Jens Christian (ORCID Profile 0000-0002-9870-4924)

Download

[img]

Version: Accepted Version

| Preview

Export / Share Citation


Statistics

Additional statistics for this record