Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Visualizing High-Dimensional Input Data With Growing Self-Organizing Maps
Year:2007
Research Areas
  • Artificial intelligence
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
JCR
No
Title
Lecture Notes in Computer Science
ISBN
0302-9743
Impact factor JCR
0
Impact info
Volume
4507
Journal number
0
From page
580
To page
587
Month
SIN MES
Ranking
Participants
  • Participante: Soledad Delgado Sanz
  • Autor: M.Estibaliz Martinez Izquierdo (UPM)
  • Autor: Agueda Arquero Hidalgo (UPM)
  • Autor: Consuelo Gonzalo Martin (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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