Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/95686
Type of publication: Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)
Field of Science: Informatika / Informatics (N009)
Author(s): Kurasova, Olga;Molytė, Alma
Title: Combination of vector quantization and visualization
Is part of: Machine learning and data mining in pattern recognition : 6th international conference, MLDM 2009 : Leipzig, Germany, July 23-25, 2009 : proceedings. Berlin; Heidelberg : Springer, 2009
Extent: p. 29-43
Date: 2009
ISBN: 9783642030697
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
Internet: https://doi.org/10.1007/978-3-642-03070-3_3
Affiliation(s): Matematikos ir informatikos institutas
Vytauto Didžiojo universitetas
Švietimo akademija
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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