A Support Tensor Train Machine

Chen, Cong; Batselier, K.; Ko, Ching Yun; Wong, Ngai

doi:10.1109/IJCNN.2019.8851985

A Support Tensor Train Machine

Conference paper (2019)

Authors

Cong Chen The University of Hong Kong

K. Batselier Team Jan-Willem van Wingerden, The University of Hong Kong

Ching Yun Ko The University of Hong Kong

Ngai Wong The University of Hong Kong

Research Group

Team Jan-Willem van Wingerden

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

Classification Support vector machine Tensor train

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Published Date

2019

Language

English

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Research Group

Team Jan-Willem van Wingerden

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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