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.
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© 1997 Springer-Verlag Berlin Heidelberg
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Herrmann, M., Villmann, T. (1997). Vector quantization by optimal neural gas. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020224
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DOI: https://doi.org/10.1007/BFb0020224
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