Observatorio de I+D+i UPM

Descripción
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
Internacional	Si
JCR del ISI	No
Título de la revista	Lecture Notes in Computer Science
ISSN	0302-9743
Factor de impacto JCR	0
Información de impacto
Volumen	4507
DOI
Número de revista	0
Desde la página	580
Hasta la página	587
Mes	SIN MES
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Esta actividad pertenece a memorias de investigación

• Participante: Soledad Delgado Sanz
• Autor: M.Estibaliz Martinez Izquierdo (UPM)
• Autor: Agueda Arquero Hidalgo (UPM)
• Autor: Consuelo Gonzalo Martin (UPM)

• Creador: Grupo de Investigación: Informática Aplicada al Procesado de Señal e Imagen
• Departamento: Arquitectura y Tecnología de Sistemas Informáticos