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Comparing methods for dimensionality reduction when data are density functions
Delicado, Pedro
Daunis i Estadella, Josep; Martín Fernández, Josep Antoni; Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada
Functional Data Analysis (FDA) deals with samples where a whole function is observedfor each individual. A particular case of FDA is when the observed functions are densityfunctions, that are also an example of infinite dimensional compositional data. In thiswork we compare several methods for dimensionality reduction for this particular typeof data: functional principal components analysis (PCA) with or without a previousdata transformation and multidimensional scaling (MDS) for diferent inter-densitiesdistances, one of them taking into account the compositional nature of density functions. The difeerent methods are applied to both artificial and real data (householdsincome distributions)
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.
Anàlisi funcional
Tots els drets reservats
Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada

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