Grouping Around Different Dimensional Affine Subspaces

Título

dc.contributor.author	García Escudero, Luis Ángel
dc.contributor.author	Gordaliza Ramos, Alfonso
dc.contributor.author	Matrán Bea, Carlos
dc.contributor.author	Mayo Iscar, Agustín
dc.date.accessioned	2016-12-20T11:40:54Z
dc.date.available	2016-12-20T11:40:54Z
dc.date.issued	2013
dc.identifier.citation	Statistical Models for Data Analysis 2013. Studies in Classification, Data Analysis, and Knowledge Organization 201, Edited by Paolo Giudici, Salvatore Ingrassia, Maurizio Vichi,	es
dc.identifier.uri	http://uvadoc.uva.es/handle/10324/21848
dc.description.abstract	Grouping around affine subspaces and other types of manifolds is receiving a lot of attention in the literature due to its interest in several fields of application. Allowing for different dimensions is needed in many applications. This work extends the TCLUST methodology to deal with the problem of grouping data around different dimensional linear subspaces in the presence of noise. Two ways of considering error terms in the orthogonal of the linear subspaces are considered.	es
dc.format.mimetype	application/pdf	es
dc.language.iso	spa	es
dc.rights.accessRights	info:eu-repo/semantics/openAccess	es
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Estadística	es
dc.title	Grouping Around Different Dimensional Affine Subspaces	es
dc.type	info:eu-repo/semantics/bookPart	es
dc.rights	Attribution 4.0 International

La licencia del ítem se describe como Attribution 4.0 International