Combining Multidimensional Scaling with Artificial Neural Networks

  • Gintautas Dzemyda
  • Olga Kurasova
  • Julius Žilinskas
Part of the Springer Optimization and Its Applications book series (SOIA, volume 75)


The combination and integrated use of data visualization methods of a different nature are under a rapid development. The combination of different methods can be applied to make a data analysis, while minimizing the shortcomings of individual methods. This chapter is devoted to visualization methods based on an artificial neural network. The fundamentals of artificial neural networks that are essential for investigating their potential to visualize multidimensional data are presented below. A biological neuron is introduced here. The model of an artificial neuron is presented, too. Structures of one-layer and multilayer feed-forward neural networks are investigated. Learning algorithms are described. Some artificial neural networks, widely used for visualization of multidimensional data, are overviewed, such as a self-organizing map, neural gas, curvilinear component analysis, auto-associative neural network, and NeuroScale. Much attention is paid to two strategies of the combination of multidimensional scaling and artificial neural network. The first of them is based on the integration of a self-organizing map or neural gas with the multidimensional scaling. The second one is based on the minimization of Stress using a feed-forward neural network SAMANN. The possibility to train the artificial neural network by multidimensional scaling results is discussed, too.


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Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Gintautas Dzemyda
    • 1
  • Olga Kurasova
    • 1
  • Julius Žilinskas
    • 1
  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania

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