Using self organizing maps on compositional data

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dc.contributor Universitat de Girona. Departament d'Informàtica i Matemàtica Aplicada
dc.contributor.author Cortés, Joaquín A.
dc.contributor.author Palma, José Luis
dc.contributor.editor Daunis i Estadella, Josep
dc.contributor.editor Martín Fernández, Josep Antoni
dc.date.issued 2008-05-28
dc.identifier.citation Cortés, J.A.; Palma, J.L. 'Using self organizing maps on compositional data' a CODAWORK’08. Girona: La Universitat, 2008 [consulta: 16 maig 2008]. Necessita Adobe Acrobat. Disponible a Internet a:
dc.identifier.uri http://hdl.handle.net/10256/740
dc.description.abstract Self-organizing maps (Kohonen 1997) is a type of artificial neural network developed to explore patterns in high-dimensional multivariate data. The conventional version of the algorithm involves the use of Euclidean metric in the process of adaptation of the model vectors, thus rendering in theory a whole methodology incompatible with non-Euclidean geometries. In this contribution we explore the two main aspects of the problem: 1. Whether the conventional approach using Euclidean metric can shed valid results with compositional data. 2. If a modification of the conventional approach replacing vectorial sum and scalar multiplication by the canonical operators in the simplex (i.e. perturbation and powering) can converge to an adequate solution. Preliminary tests showed that both methodologies can be used on compositional data. However, the modified version of the algorithm performs poorer than the conventional version, in particular, when the data is pathological. Moreover, the conventional ap- proach converges faster to a solution, when data is \well-behaved". Key words: Self Organizing Map; Artificial Neural networks; Compositional data
dc.description.sponsorship Geologische Vereinigung; Institut d’Estadística de Catalunya; International Association for Mathematical Geology; Càtedra Lluís Santaló d’Aplicacions de la Matemàtica; Generalitat de Catalunya, Departament d’Innovació, Universitats i Recerca; Ministerio de Educación y Ciencia; Ingenio 2010.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada
dc.rights Tots els drets reservats
dc.subject Meteorologia -- Models estadístics
dc.subject Anàlisi multivariable
dc.title Using self organizing maps on compositional data
dc.type info:eu-repo/semantics/conferenceObject


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