Vector quantization by optimal neural gas

M. Herrmann, Th. Villmann

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Many vector quantization algorithms have been designed to minimize the reconstruction error of the data representation. The additional requirement of topology preservation in self-organizing maps conflicts this goal but can be alleviated by suitable modifications. In the present contribution we demonstrate that the neural gas algorithm allows for vector quantization with a theoretically optimal reconstruction error over an extended range of parameters. Moreover, by a similar scheme as previously applied to self-organizing maps it is possible to modify the neural gas algorithm such as to meet optimality criteria other than the reconstruction error in a way which is exact for arbitrary dimensionality of the data.
Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN'97
Subtitle of host publication7th International Conference Lausanne, Switzerland, October 8–10, 1997 Proceeedings
EditorsWulfram Gerstner, Alain Germond, Martin Hasler, Jean-Daniel Nicoud
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Pages625-630
Number of pages6
ISBN (Electronic)978-3-540-69620-9
ISBN (Print)978-3-540-63631-1
DOIs
Publication statusPublished - 1997

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume1327
ISSN (Print)0302-9743

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