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Canonical correlation analysis of high-dimensional data with very small sample support

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorSong, Yang
dc.contributor.authorSchreier, Peter J.
dc.contributor.authorRamírez García, David
dc.contributor.authorHasija, Tanuj
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2020-11-24T12:30:41Z
dc.date.available2020-11-24T12:30:41Z
dc.date.issued2016-11-01
dc.description.abstractThis paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is very small, possibly much smaller than the dimensions of the data. In such a scenario, a principal component analysis (PCA) rank-reduction preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present simple, yet very effective, approaches to the joint model-order selection of the number of dimensions that should be retained through the PCA step and the number of correlated signals. These approaches are based on reduced-rank versions of the Bartlett-Lawley hypothesis test and the minimum description length information-theoretic criterion. Simulation results show that the techniques perform well for very small sample sizes even in colored noise. (C) 2016 Elsevier B.V. All rights reserved.en
dc.description.sponsorshipThis research was supported by the German Research Foun-dation (DFG) under grant SCHR 1384/3-1, and the Alfried Kruppvon Bohlen und Halbach foundation under its program “Return ofGerman scientists from abroad”. The work of D. Ramírez has been partly supported by Ministerio de Economía of Spain under projects: COMPREHENSION (TEC2012-38883-C02-01), OTOSIS(TEC2013-41718-R), and the COMONSENS Network (TEC2015-69648-REDC), by the Ministerio de Economía of Spain jointly withthe European Commission (ERDF) under project ADVENTURE(TEC2015-69868-C2-1-R), and by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845).en
dc.identifier.bibliographicCitationSong, Y., Schreier, P. J., Ramírez, D., & Hasija, T. (2016). Canonical correlation analysis of high-dimensional data with very small sample support. Signal Processing, 128, 449-458
dc.identifier.doihttps://doi.org/10.1016/j.sigpro.2016.05.020
dc.identifier.issn0165-1684
dc.identifier.publicationfirstpage449
dc.identifier.publicationlastpage458
dc.identifier.publicationtitleSignal Processing
dc.identifier.publicationvolume128
dc.identifier.urihttps://hdl.handle.net/10016/31469
dc.identifier.uxxiAR/0000018112
dc.language.isoeng
dc.publisherElsevier
dc.relation.projectIDGobierno de España. TEC2012-38883-C02-01
dc.relation.projectIDGobierno de España. TEC2013-41718-Res
dc.relation.projectIDGobierno de España. TEC2015-69648-REDC
dc.relation.projectIDGobierno de España. TEC2015-69868-C2-1-Res
dc.relation.projectIDComunidad de Madrid. S2013/ICE-2845es
dc.rights© Elsevier, 2016
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherBartlett-Lawley statisticen
dc.subject.otherCanonical correlation analysisen
dc.subject.otherModel-order selectionen
dc.subject.otherPrincipal component analysisen
dc.subject.otherSmall sample supporten
dc.subject.otherInformation-theoretic criteriaen
dc.subject.otherSignalsen
dc.subject.otherNoiseen
dc.subject.otherNumberen
dc.subject.otherComponentsen
dc.titleCanonical correlation analysis of high-dimensional data with very small sample supporten
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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