A Support Tensor Train Machine

Cong Chen; Kim Batselier; Ching Yun Ko; Ngai Wong

doi:10.1109/IJCNN.2019.8851985

A Support Tensor Train Machine

Cong Chen, Kim Batselier, Ching Yun Ko, Ngai Wong

Team Jan-Willem van Wingerden

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

8 Citations (Scopus)

28 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019)
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Number of pages	8
ISBN (Electronic)	978-1-7281-1985-4
ISBN (Print)	978-1-7281-2009-6
DOIs	https://doi.org/10.1109/IJCNN.2019.8851985
Publication status	Published - 2019

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

classification
support vector machine
tensor train

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10.1109/IJCNN.2019.8851985

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Cite this

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title = "A Support Tensor Train Machine",

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.",

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PY - 2019

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N2 - 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.

AB - 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.

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DO - 10.1109/IJCNN.2019.8851985

M3 - Conference contribution

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BT - Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019)

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A Support Tensor Train Machine

Abstract

Bibliographical note

Keywords

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