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Article: Parallelized Tensor Train Learning of Polynomial Classifiers

TitleParallelized Tensor Train Learning of Polynomial Classifiers
Authors
KeywordsPattern classification
Polynomial classifier
Supervised learning
Tensor train (TT)
Issue Date2018
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2018, v. 29 n. 10, p. 4621-4632 How to Cite?
AbstractIn pattern classification, polynomial classifiers are well-studied methods as they are capable of generating complex decision surfaces. Unfortunately, the use of multivariate polynomials is limited to kernels as in support-vector machines, because polynomials quickly become impractical for high-dimensional problems. In this paper, we effectively overcome the curse of dimensionality by employing the tensor train (TT) format to represent a polynomial classifier. Based on the structure of TTs, two learning algorithms are proposed, which involve solving different optimization problems of low computational complexity. Furthermore, we show how both regularization to prevent overfitting and parallelization, which enables the use of large training sets, are incorporated into these methods. The efficiency and efficacy of our tensor-based polynomial classifier are then demonstrated on the two popular data sets U.S. Postal Service and Modified NIST.
Persistent Identifierhttp://hdl.handle.net/10722/261767
ISSN
2017 Impact Factor: 7.982
2015 SCImago Journal Rankings: 3.181
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Z-
dc.contributor.authorBatselier, K-
dc.contributor.authorSuykens, JAK-
dc.contributor.authorWong, N-
dc.date.accessioned2018-09-28T04:47:31Z-
dc.date.available2018-09-28T04:47:31Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2018, v. 29 n. 10, p. 4621-4632-
dc.identifier.issn2162-237X-
dc.identifier.urihttp://hdl.handle.net/10722/261767-
dc.description.abstractIn pattern classification, polynomial classifiers are well-studied methods as they are capable of generating complex decision surfaces. Unfortunately, the use of multivariate polynomials is limited to kernels as in support-vector machines, because polynomials quickly become impractical for high-dimensional problems. In this paper, we effectively overcome the curse of dimensionality by employing the tensor train (TT) format to represent a polynomial classifier. Based on the structure of TTs, two learning algorithms are proposed, which involve solving different optimization problems of low computational complexity. Furthermore, we show how both regularization to prevent overfitting and parallelization, which enables the use of large training sets, are incorporated into these methods. The efficiency and efficacy of our tensor-based polynomial classifier are then demonstrated on the two popular data sets U.S. Postal Service and Modified NIST.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72-
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems-
dc.rightsIEEE Transactions on Neural Networks and Learning Systems. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectPattern classification-
dc.subjectPolynomial classifier-
dc.subjectSupervised learning-
dc.subjectTensor train (TT)-
dc.titleParallelized Tensor Train Learning of Polynomial Classifiers-
dc.typeArticle-
dc.identifier.emailBatselier, K: kbatseli@HKUCC-COM.hku.hk-
dc.identifier.emailWong, N: nwong@eee.hku.hk-
dc.identifier.authorityWong, N=rp00190-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TNNLS.2017.2771264-
dc.identifier.pmid29990204-
dc.identifier.scopuseid_2-s2.0-85037628783-
dc.identifier.hkuros292456-
dc.identifier.volume29-
dc.identifier.issue10-
dc.identifier.spage4621-
dc.identifier.epage4632-
dc.identifier.isiWOS:000445351300006-
dc.publisher.placeUnited States-

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