@inbook{445db74500844a64af6929ac2a55ca4e,
title = "Vector quantization by optimal neural gas",
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.",
author = "M. Herrmann and Th. Villmann",
year = "1997",
doi = "10.1007/BFb0020224",
language = "English",
isbn = "978-3-540-63631-1",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "625--630",
editor = "Wulfram Gerstner and Alain Germond and Martin Hasler and Jean-Daniel Nicoud",
booktitle = "Artificial Neural Networks - ICANN'97",
address = "United Kingdom",
}