Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Visualizing High-Dimensional Input Data With Growing Self-Organizing Maps
Year:2007
Research Areas
  • Geology
Information
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
International
Si
Congress
Sensing and understanding our planet
960
Place
Reviewers
Si
ISBN/ISSN
1-4244-1212-9
Start Date
End Date
From page
To page
Participants
  • Participante: Soledad Delgado Sanz
  • Autor: Consuelo Gonzalo Martin (UPM)
  • Autor: M.Estibaliz Martinez Izquierdo (UPM)
  • Autor: Agueda Arquero Hidalgo (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Informática Aplicada al Procesado de Señal e Imagen
  • Departamento: Arquitectura y Tecnología de Sistemas Informáticos
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