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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M3R5NP
Repositorysid.inpe.br/sibgrapi/2016/07.11.21.17
Last Update2016:07.11.21.17.29 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/07.11.21.17.29
Metadata Last Update2022:06.14.00.08.22 (UTC) administrator
DOI10.1109/SIBGRAPI.2016.056
Citation KeyFilisbinoGiraThom:2016:RaPrCo
TitleRanking Principal Components in Face Spaces Through AdaBoost.M2 Linear Ensemble
FormatOn-line
Year2016
Access Date2024, Apr. 27
Number of Files1
Size356 KiB
2. Context
Author1 Filisbino, Tiene Andre
2 Giraldi, Gilson Antonio
3 Thomaz, Carlos Eduardo
Affiliation1 National Laboratory for Scientific Computing
2 National Laboratory for Scientific Computing
3 Department of Electrical Engineering - FEI
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addresstiene@lncc.br
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherIEEE Computer Society´s Conference Publishing Services
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2016-07-11 21:17:29 :: tiene@lncc.br -> administrator ::
2016-10-05 14:49:11 :: administrator -> tiene@lncc.br :: 2016
2016-10-13 13:54:38 :: tiene@lncc.br -> administrator :: 2016
2022-06-14 00:08:22 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDiscriminant Analysis
Principal Component Analysis
Support Vector Machine
Ensemble Methods
AdaBoost
AbstractDespite the success of Principal Component Analysis (PCA) for dimensionality reduction, it is known that its most expressive components do not necessarily represent important discriminant features for pattern recognition. In this paper, the problem of ranking PCA components, computed from multi-class databases, is addressed by building multiple linear learners that are combined through the AdaBoost.M2 in order to determine the discriminant contribution of each PCA feature. In our implementation, each learner is a weakened version of a linear support vector machine (SVM). The strong learner built by the ensemble technique is processed following a strategy to get the global discriminant vector to sort PCA components according to their relevance for classification tasks. Also, we show how the proposed methodology to compute the global discriminant vector can be applied to other multi-class approaches, like the linear discriminant analysis (LDA). In the computational experiments we compare the obtained approaches with counterpart ones using facial expression experiments. Our experimental results have shown that the principal components selected by the proposed technique allows higher recognition rates using less linear features.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2016 > Ranking Principal Components...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Ranking Principal Components...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M3R5NP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M3R5NP
Languageen
Target FilePID4355033.pdf
User Grouptiene@lncc.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 6
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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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