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

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dc.contributor.author Song, Yang
dc.contributor.author Schreier, Peter J.
dc.contributor.author Ramírez García, David
dc.contributor.author Hasija, Tanuj
dc.date.accessioned 2020-11-24T12:30:41Z
dc.date.available 2020-11-24T12:30:41Z
dc.date.issued 2016-11-01
dc.identifier.bibliographicCitation Song, 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.issn 0165-1684
dc.identifier.uri http://hdl.handle.net/10016/31469
dc.description.abstract This 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.
dc.description.sponsorship This 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).
dc.language.iso eng
dc.publisher Elsevier
dc.rights © Elsevier, 2016
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Bartlett-Lawley statistic
dc.subject.other Canonical correlation analysis
dc.subject.other Model-order selection
dc.subject.other Principal component analysis
dc.subject.other Small sample support
dc.subject.other Information-theoretic criteria
dc.subject.other Signals
dc.subject.other Noise
dc.subject.other Number
dc.subject.other Components
dc.title Canonical correlation analysis of high-dimensional data with very small sample support
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1016/j.sigpro.2016.05.020
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2012-38883-C02-01
dc.relation.projectID Gobierno de España. TEC2013-41718-R
dc.relation.projectID Gobierno de España. TEC2015-69648-REDC
dc.relation.projectID Gobierno de España. TEC2015-69868-C2-1-R
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2845
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 449
dc.identifier.publicationlastpage 458
dc.identifier.publicationtitle Signal Processing
dc.identifier.publicationvolume 128
dc.identifier.uxxi AR/0000018112
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Comunidad de Madrid
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