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Dimensionality reduction with image data

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorBenito Bonito, Mónica
dc.contributor.authorPeña, Daniel
dc.date.accessioned2006-11-09T10:55:49Z
dc.date.available2006-11-09T10:55:49Z
dc.date.issued2004-02
dc.description.abstractA common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a Procrustes rotation and show that it leads to a better reconstruction of images.
dc.format.extent736268 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.repecws041003
dc.identifier.urihttps://hdl.handle.net/10016/207
dc.language.isoeng
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries2004-03
dc.rights.accessRightsopen access
dc.subject.ecienciaEstadística
dc.titleDimensionality reduction with image data
dc.typeworking paper*
dspace.entity.typePublication
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