A Support Tensor Train Machine

A Support Tensor Train Machine

Chen, Cong (The University of Hong Kong)
Batselier, K. (TU Delft Team Jan-Willem van Wingerden; The University of Hong Kong)
Ko, Ching Yun (The University of Hong Kong)
Wong, Ngai (The University of Hong Kong)

2019

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.

classification
support vector machine
tensor train

http://resolver.tudelft.nl/uuid:15a3be69-1852-4f85-bf8d-1bd9b45c7cb9

https://doi.org/10.1109/IJCNN.2019.8851985

IEEE, Piscataway, NJ, USA

2020-03-30

978-1-7281-2009-6

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

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.

Institutional Repository

conference paper

© 2019 Cong Chen, K. Batselier, Ching Yun Ko, Ngai Wong