Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Topology preserving visualization methods for growing self-organizing maps
Year:2009
Research Areas
  • Processing and signal analysis
Information
Abstract
Self-organizing map (SOM) is a neural network model widely used in high dimensional data visualization processes. A trained SOM provides a simplified data model as well as a projection of the multidimensional input data into a bi-dimensional plane that reflects the relationships involving the training patters. Visualization methods based in SOM explore different characteristics related to the data learned by the network. It is necessary to find methods to determine the goodness of a trained network in order to evaluate the quality of the high dimensional data visualizations generated using the SOM simplified model. The degree of topology preservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topology preservation, in particular using Kohonen model. In this work, two measuring topology preservation methods for Growing Cell Structures (GCS) model are proposed: the topographic function and the topology preserving map.
International
Si
JCR
No
Title
Lecture notes in computer science
ISBN
0302-9743
Impact factor JCR
0
Impact info
Volume
5517
10.1007/978-3-642-02478-8
Journal number
1
From page
197
To page
204
Month
JUNIO
Ranking
Participants
  • Autor: Maria Soledad Delgado Sanz (UPM)
  • Autor: Agueda Arquero Hidalgo (UPM)
  • Autor: Consuelo Gonzalo Martin (UPM)
  • Autor: M.Estibaliz Martinez Izquierdo (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Informática Aplicada al Procesado de Señal e Imagen
  • Departamento: Organización y Estructura de la Información
  • Departamento: Arquitectura y Tecnología de Sistemas Informáticos
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