Publication:
A novel framework for parsimonious multivariate analysis

dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.contributor.authorMuñoz Romero, Sergio
dc.contributor.authorGómez Verdejo, Vanessa
dc.contributor.authorParrado Hernández, Emilio
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-12-23T12:21:27Z
dc.date.available2021-12-23T12:21:27Z
dc.date.issued2017-11
dc.description.abstractThis paper proposes a framework in which a multivariate analysis method (MVA) guides a selection of input variables that leads to a sparse feature extraction. This framework, called parsimonious MVA, is specially suited for high dimensional data such as gene arrays, digital pictures, etc. The feature selection relies on the analysis of consistency in the behaviour of the input variables through the elements of an ensemble of MVA projection matrices. The ensemble is constructed following a bootstrap that builds on an efficient and generalized MVA formulation that covers PCA, CCA and OPLS. Moreover, it allows the estimation of the relative relevance of each selected input variable. Experimental results point out that the features extracted by the parsimonious MVA have excellent discrimination power, comparing favorably with state-of-the-art methods, and are potentially useful to build interpretable features. Besides, the parsimonious feature extractor is shown to be robust against to parameter selection, as we all computationally efficient.en
dc.description.sponsorshipThis work has been partly funded by the Spanish MINECO grant TEC2014-52289R and TEC2013-48439-C4-1-R. The authors want to thank the action editor and the reviewers for their valuable feedback.en
dc.format.extent14
dc.identifier.bibliographicCitationMuñoz-Romero, S., Gómez-Verdejo, V. & Parrado-Hernández, E. (2017). A novel framework for parsimonious multivariate analysis. Pattern Recognition, 71, 173–186.en
dc.identifier.doihttps://doi.org/10.1016/j.patcog.2017.06.004
dc.identifier.issn0031-3203
dc.identifier.publicationfirstpage173
dc.identifier.publicationlastpage186
dc.identifier.publicationtitlePattern Recognitionen
dc.identifier.publicationvolume71
dc.identifier.urihttps://hdl.handle.net/10016/33842
dc.identifier.uxxiAR/0000020457
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. TEC2014-52289-Res
dc.relation.projectIDGobierno de España. TEC2013-48439-C4-1-Res
dc.rights© 2017 Elsevier Ltd. All rights reserved.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherFeature selectionen
dc.subject.otherDimensionality reductionen
dc.subject.otherMultivariate analysisen
dc.subject.otherPrincipal component analysisen
dc.subject.otherCanonical correlation analysisen
dc.subject.otherOrthonormalized partial least squaresen
dc.titleA novel framework for parsimonious multivariate analysisen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Novel_PR_2017_ps.pdf
Size:
1.38 MB
Format:
Adobe Portable Document Format