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- Publisher Website: 10.1109/IJCNN.2019.8851985
- Scopus: eid_2-s2.0-85073245907
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Conference Paper: A support tensor train machine
Title | A support tensor train machine |
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Authors | |
Keywords | classification support vector machine tensor train |
Issue Date | 2019 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500 |
Citation | Proceedings of 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14-19 July 2019 How to Cite? |
Abstract | There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. Support tensor machine (STM) and support Tucker machine (STuM) are two typical tensor generalization of the conventional support vector machine (SVM). However, the expressive power of STM is restrictive due to its rank-one tensor constraint, and STuM is not scalable because of the exponentially sized Tucker core tensor. To overcome these limitations, we introduce a novel and effective support tensor train machine (STTM) by employing a general and scalable tensor train as the parameter model. Experiments validate and confirm the superiority of the STTM over SVM, STM and STuM. |
Persistent Identifier | http://hdl.handle.net/10722/275280 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Chen, C | - |
dc.contributor.author | Batselier, K | - |
dc.contributor.author | Ko, CY | - |
dc.contributor.author | Wong, N | - |
dc.date.accessioned | 2019-09-10T02:39:20Z | - |
dc.date.available | 2019-09-10T02:39:20Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14-19 July 2019 | - |
dc.identifier.isbn | 978-1-7281-1986-1 | - |
dc.identifier.uri | http://hdl.handle.net/10722/275280 | - |
dc.description.abstract | There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. Support tensor machine (STM) and support Tucker machine (STuM) are two typical tensor generalization of the conventional support vector machine (SVM). However, the expressive power of STM is restrictive due to its rank-one tensor constraint, and STuM is not scalable because of the exponentially sized Tucker core tensor. To overcome these limitations, we introduce a novel and effective support tensor train machine (STTM) by employing a general and scalable tensor train as the parameter model. Experiments validate and confirm the superiority of the STTM over SVM, STM and STuM. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500 | - |
dc.relation.ispartof | International Joint Conference on Neural Networks (IJCNN) | - |
dc.rights | International Joint Conference on Neural Networks (IJCNN). Copyright © IEEE. | - |
dc.rights | ©2019 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 | classification | - |
dc.subject | support vector machine | - |
dc.subject | tensor train | - |
dc.title | A support tensor train machine | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wong, N: nwong@eee.hku.hk | - |
dc.identifier.authority | Wong, N=rp00190 | - |
dc.identifier.doi | 10.1109/IJCNN.2019.8851985 | - |
dc.identifier.scopus | eid_2-s2.0-85073245907 | - |
dc.identifier.hkuros | 304919 | - |
dc.publisher.place | United States | - |