Observatorio de I+D+i UPM

Descripción
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.
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	5517
DOI	10.1007/978-3-642-02478-8
Número de revista	1
Desde la página	197
Hasta la página	204
Mes	JUNIO
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Esta actividad pertenece a memorias de investigación

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

• 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