The Advance of Support Tensor Machine

In recent years, tensor-based machine learning methods, in which the Support Tensor Machine (STM) is a typical technology, have gradually attracted the attention of researchers. Compared with Support Vector Machine (SVM), STM has superior generalization ability that can make full use of the structur...

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Bibliographic Details
Published in2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) pp. 121 - 128
Main Authors Xiang, Yi, Jiang, Qian, He, Jing, Jin, Xin, Wu, LiWen, Yao, Shaowen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Classification Algorithm
Classification algorithms
Computational modeling
Machine learning
Machine learning algorithms
Matrix decomposition
Support Tensor Machine
Support vector machines
Tensile stress
Tensor Decomposition
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Summary:In recent years, tensor-based machine learning methods, in which the Support Tensor Machine (STM) is a typical technology, have gradually attracted the attention of researchers. Compared with Support Vector Machine (SVM), STM has superior generalization ability that can make full use of the structural information of data. However, it still faces many challenges due to the imperfection of its theoretical basis and model. In order to study the further development of STM, this paper provides a survey about the potential and existing problems in STM.
DOI:10.1109/SERA.2018.8477228