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- Publisher Website: 10.1109/TNNLS.2017.2771264
- Scopus: eid_2-s2.0-85037628783
- PMID: 29990204
- WOS: WOS:000445351300006
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Article: Parallelized Tensor Train Learning of Polynomial Classifiers
Title | Parallelized Tensor Train Learning of Polynomial Classifiers |
---|---|
Authors | |
Keywords | Pattern classification Polynomial classifier Supervised learning Tensor train (TT) |
Issue Date | 2018 |
Publisher | IEEE. 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? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/261767 |
ISSN | 2023 Impact Factor: 10.2 2023 SCImago Journal Rankings: 4.170 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, Z | - |
dc.contributor.author | Batselier, K | - |
dc.contributor.author | Suykens, JAK | - |
dc.contributor.author | Wong, N | - |
dc.date.accessioned | 2018-09-28T04:47:31Z | - |
dc.date.available | 2018-09-28T04:47:31Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2018, v. 29 n. 10, p. 4621-4632 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | http://hdl.handle.net/10722/261767 | - |
dc.description.abstract | In 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.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72 | - |
dc.relation.ispartof | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.rights | IEEE 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.subject | Pattern classification | - |
dc.subject | Polynomial classifier | - |
dc.subject | Supervised learning | - |
dc.subject | Tensor train (TT) | - |
dc.title | Parallelized Tensor Train Learning of Polynomial Classifiers | - |
dc.type | Article | - |
dc.identifier.email | Batselier, K: kbatseli@HKUCC-COM.hku.hk | - |
dc.identifier.email | Wong, N: nwong@eee.hku.hk | - |
dc.identifier.authority | Wong, N=rp00190 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TNNLS.2017.2771264 | - |
dc.identifier.pmid | 29990204 | - |
dc.identifier.scopus | eid_2-s2.0-85037628783 | - |
dc.identifier.hkuros | 292456 | - |
dc.identifier.volume | 29 | - |
dc.identifier.issue | 10 | - |
dc.identifier.spage | 4621 | - |
dc.identifier.epage | 4632 | - |
dc.identifier.isi | WOS:000445351300006 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2162-237X | - |