Abstract
In this paper, we present a comparative analysis of a combination of two vector quantization methods (self-organizing map and neural gas), based on a neural network and multidimensional scaling that is used for visualization of codebook vectors obtained by vector quantization methods. The dependence of computing time on the number of neurons, the ratio between the number of neuron-winners and that of all neurons, quantization and mapping qualities, and preserving of a data structure in the mapping image are investigated.
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Kurasova, O., Molytė, A. (2009). Combination of Vector Quantization and Visualization. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_3
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DOI: https://doi.org/10.1007/978-3-642-03070-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03069-7
Online ISBN: 978-3-642-03070-3
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