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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43BAC95
Repositorysid.inpe.br/sibgrapi/2020/09.29.09.50
Last Update2020:09.29.09.50.28 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.29.09.50.28
Metadata Last Update2022:06.14.00.00.11 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00046
Citation KeyCervatiNetoLeva:2020:PaApUn
TitleISOMAP-KL: a parametric approach for unsupervised metric learning
FormatOn-line
Year2020
Access Date2024, Sep. 16
Number of Files1
Size427 KiB
2. Context
Author1 Cervati Neto, Alaor
2 Levada, Alexandre Luis Magalhães
Affiliation1 Federal University of São Carlos
2 Federal University of São Carlos
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressalaor_c_neto@yahoo.com.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-29 09:50:28 :: alaor_c_neto@yahoo.com.br -> administrator ::
2022-06-14 00:00:11 :: administrator -> alaor_c_neto@yahoo.com.br :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordspattern recognition
manifold learning
AbstractUnsupervised metric learning consists in building data-specific similarity measures without information of the class labels. Dimensionality reduction (DR) methods have shown to be a powerful mathematical tool for uncovering the underlying geometric structure of data. Manifold learning algorithms are capable of finding a more compact representation for data in the presence of non-linearities. However, one limitation is that most of them are pointwise methods, in the sense that they are not robust to the presence of outliers and noise in data. In this paper, we present ISOMAP-KL, a parametric patch-based algorithm that uses the KL-divergence between local Gaussian distributions learned from neighborhood systems along the KNN graph. We use this non-Euclidean measure to compute the weights and define the entropic KNN graph, whose shortest paths approximate the geodesic distances between patches of points in a parametric feature space. Results obtained in several datasets show that the proposed method is capable of improving the classification accuracy in comparison to other DR methods.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/43BAC95
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43BAC95
Languageen
Target FilePID6629767.pdf
User Groupalaor_c_neto@yahoo.com.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 28
sid.inpe.br/sibgrapi/2022/06.10.21.49 3
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
7. Description control
e-Mail (login)alaor_c_neto@yahoo.com.br
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