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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3EE5MJ5
Repositorysid.inpe.br/sibgrapi/2013/07.08.14.20
Last Update2013:07.08.14.20.39 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2013/07.08.14.20.39
Metadata Last Update2022:06.14.00.07.45 (UTC) administrator
DOI10.1109/SIBGRAPI.2013.50
Citation KeyFilisbinoGiraThom:2013:RaMeTe
TitleRanking Methods for Tensor Components Analysis and their Application to Face Images
FormatOn-line.
Year2013
Access Date2024, May 03
Number of Files1
Size2935 KiB
2. Context
Author1 Filisbino, Tiene Andre
2 Giraldi, Antonio Giraldi
3 Thomaz, Carlos Eduardo
Affiliation1 National Laboratory for Scientific Computing
2 National Laboratory for Scientific Computing
3 Department of Electrical Engineering FEI
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
e-Mail Addresstiene@lncc.br
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Date5-8 Aug. 2013
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2013-07-08 14:20:39 :: tiene@lncc.br -> administrator ::
2022-06-14 00:07:45 :: administrator -> :: 2013
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDimensionality Reduction
Tensor Subspace Learning
CSA
Face Image Analysis
AbstractHigher order tensors have been applied to model multidimensional image databases for subsequent tensor decomposition and dimensionality reduction. In this paper we address the problem of ranking tensor components in the context of the concurrent subspace analysis (CSA) technique following two distinct approaches: (a) Estimating the covariance structure of the database; (b) Computing discriminant weights through separating hyperplanes, to select the most discriminant CSA tensor components. The former follows a ranking method based on the covariance structure of each subspace in the CSA framework while the latter addresses the problem through the discriminant principal component analysis methodology. Both approaches are applied and compared in a gender classification task performed using the FEI face database. Our experimental results highlight the low dimensional data representation of both approaches, while allowing robust discriminant reconstruction and interpretation of the sample groups and high recognition rates.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2013 > Ranking Methods for...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Ranking Methods for...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3EE5MJ5
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3EE5MJ5
Languageen
Target FileSibgrapi_2013.pdf
User Grouptiene@lncc.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SLB4P
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.04.02 7
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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