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Efficient Data Projection for Visual Analysis of Large Data Sets Using Neural Networks
Volume 22, Issue 4 (2011), pp. 507–520
Viktor Medvedev   Gintautas Dzemyda   Olga Kurasova   Virginijus Marcinkevičius  

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https://doi.org/10.15388/Informatica.2011.339
Pub. online: 1 January 2011      Type: Research Article     

Received
1 September 2011
Accepted
1 December 2011
Published
1 January 2011

Abstract

The most classical visualization methods, including multidimensional scaling and its particular case – Sammon's mapping, encounter difficulties when analyzing large data sets. One of possible ways to solve the problem is the application of artificial neural networks. This paper presents the visualization of large data sets using the feed-forward neural network – SAMANN. This back propagation-like learning rule has been developed to allow a feed-forward artificial neural network to learn Sammon's mapping in an unsupervised way. In its initial form, SAMANN training is computation expensive. In this paper, we discover conditions optimizing the computational expenditure in visualization even of large data sets. It is shown possibility to reduce the original dimensionality of data to a lower one using small number of iterations. The visualization results of real-world data sets are presented.

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Keywords
large multidimensional data sets SAMANN neural network visualization

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INFORMATICA

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