Abstract
We investigate the neural gas quantizer in the light of statistical physics concepts. We show that this algorithm can be extended to a vector quantizer with general differentiable similarity measure offering a greater flexibility. Further, we show that the neighborhood cooperativeness control parameter is not equivalent to an inverse temperature like in the deterministic annealing vector quantizer introduced by K. Rose et al. Instead, an annealed variant of neural gas can be obtained using the formalism proposed by T. Heskes for self-organizing maps.
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Villmann, T., Hammer, B., Biehl, M. (2009). Some Theoretical Aspects of the Neural Gas Vector Quantizer. In: Biehl, M., Hammer, B., Verleysen, M., Villmann, T. (eds) Similarity-Based Clustering. Lecture Notes in Computer Science(), vol 5400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01805-3_2
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DOI: https://doi.org/10.1007/978-3-642-01805-3_2
Publisher Name: Springer, Berlin, Heidelberg
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