Efficient Data Projection for Visual Analysis of Large Data Sets Using Neural Networks

Article type: Research Article

Authors: Medvedev, Viktor | Dzemyda, Gintautas | Kurasova, Olga | Marcinkevičius, Virginijus

Affiliations: Vilnius University, Institute of Mathematics and Informatics, Akademijos 4, LT-08663 Vilnius, Lithuania, e-mail: {viktor.medvedev, gintautas.dzemyda, olga.kurasova, virginijus.marcinkevicius}@mii.vu.lt

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.

Keywords: large multidimensional data sets, SAMANN, neural network, visualization

Journal: Informatica, vol. 22, no. 4, pp. 507-520, 2011