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dc.contributor.authorWang, Deqing
dc.contributor.authorCong, Fengyu
dc.contributor.authorRistaniemi, Tapani
dc.date.accessioned2019-04-25T07:16:26Z
dc.date.available2019-04-25T07:16:26Z
dc.date.issued2019fi
dc.identifier.citationWang, D., Cong, F., & Ristaniemi, T. (2019). Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm. In <em>ICASSP 2019 : Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing</em> (pp. 3457-3461). IEEE. <a href="https://doi.org/10.1109/ICASSP.2019.8683217">doi:10.1109/ICASSP.2019.8683217</a>fi
dc.identifier.otherTUTKAID_81266
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/63614
dc.description.abstractTensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higherorder (order > 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from poor convergence and low speed. In this study, we propose a new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method in alternating nonnegative least squares (ANLS) framework is employed to minimize the objective function. Our method can guarantee the convergence and accelerate the computation. The results of experiments on both synthetic and real data demonstrate the efficiency and superiority of our method.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofICASSP 2019 : Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing
dc.rightsIn Copyright
dc.subject.othertensor decompositionfi
dc.subject.othernonnegative CAN-DECOMP/PARAFACfi
dc.subject.otherproximal algorithmfi
dc.subject.otherblock principal pivotingfi
dc.subject.otheralternating nonnegative least squaresfi
dc.titleHigher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithmfi
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201904232246
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2019-04-23T15:15:06Z
dc.relation.isbn978-1-4799-8131-1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange3457-3461
dc.relation.issn1520-6149
dc.type.versionacceptedVersion
dc.rights.copyright© 2019 IEEE.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceIEEE International Conference on Acoustics, Speech and Signal Processing
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/ICASSP.2019.8683217


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