File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Orthogonal Nonnegative Tucker Decomposition

TitleOrthogonal Nonnegative Tucker Decomposition
Authors
Pan, JNg, KPLiu, YEZhang, XYan, H
KeywordsNonnegative tensor
Tucker decomposition
Image processing
Issue Date2021
PublisherSociety for Industrial and Applied Mathematics. The Journal's web site is located at https://www.siam.org/Publications/Journals/SIAM-journal-on-scientific-computing-sisc
Citation
SIAM Journal on Scientific Computing, 2021, v. 43 n. 1, p. B55-B81 How to Cite?
DOI: http://dx.doi.org/10.1137/19M1294708
AbstractIn this paper, we study nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the optimization problem. The convergence of the algorithm is given. We employ ONTD on the image data sets from the real world applications including face recognition, image representation, and hyperspectral unmixing. Numerical results are shown to illustrate the effectiveness of the proposed algorithm.
Persistent Identifierhttp://hdl.handle.net/10722/303960
ISSN
1064-8275
2021 Impact Factor: 2.968
2020 SCImago Journal Rankings: 1.674
ISI Accession Number ID
WOS:000623833100012

 

DC FieldValueLanguage
dc.contributor.authorPan, J-
dc.contributor.authorNg, KP-
dc.contributor.authorLiu, YE-
dc.contributor.authorZhang, X-
dc.contributor.authorYan, H-
dc.date.accessioned2021-09-23T08:53:14Z-
dc.date.available2021-09-23T08:53:14Z-
dc.date.issued2021-
dc.identifier.citationSIAM Journal on Scientific Computing, 2021, v. 43 n. 1, p. B55-B81-
dc.identifier.issn1064-8275-
dc.identifier.urihttp://hdl.handle.net/10722/303960-
dc.description.abstractIn this paper, we study nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the optimization problem. The convergence of the algorithm is given. We employ ONTD on the image data sets from the real world applications including face recognition, image representation, and hyperspectral unmixing. Numerical results are shown to illustrate the effectiveness of the proposed algorithm.-
dc.languageeng-
dc.publisherSociety for Industrial and Applied Mathematics. The Journal's web site is located at https://www.siam.org/Publications/Journals/SIAM-journal-on-scientific-computing-sisc-
dc.relation.ispartofSIAM Journal on Scientific Computing-
dc.subjectNonnegative tensor-
dc.subjectTucker decomposition-
dc.subjectImage processing-
dc.titleOrthogonal Nonnegative Tucker Decomposition-
dc.typeArticle-
dc.identifier.emailNg, KP: michael.ng@hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.identifier.authorityNg, KP=rp02578-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1137/19M1294708-
dc.identifier.scopuseid_2-s2.0-85102809410-
dc.identifier.hkuros325159-
dc.identifier.volume43-
dc.identifier.issue1-
dc.identifier.spageB55-
dc.identifier.epageB81-
dc.identifier.isiWOS:000623833100012-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats