Visualizing high-dimensional input data with growing self-organizing maps

Authors:
Soledad Delgado;Consuelo Gonzalo;Estibaliz Martinez;Agueda Arquero
Affiliations:
Department of Applied Computer Science, Technical University of Madrid, Madrid, Spain;Department of Architecture and Technology of Computer Systems, Technical University of Madrid, Madrid, Spain;Department of Architecture and Technology of Computer Systems, Technical University of Madrid, Madrid, Spain;Department of Architecture and Technology of Computer Systems, Technical University of Madrid, Madrid, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

Currently, there exist many research areas that produce large multivariable datasets that are difficult to visualize in order to extract useful information. Kohonen self-organizing maps have been used successfully in the visualization and analysis of multidimensional data. In this work, a projection technique that compresses multidimensional datasets into two dimensional space using growing self-organizing maps is described. With this embedding scheme, traditional Kohonen visualization methods have been implemented using growing cell structures networks. New graphical map displays have been compared with Kohonen graphs using two groups of simulated data and one group of real multidimensional data selected from a satellite scene.