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.
To overcome this, Ref. REF proposed a support tensor train machine (STTM), which assumes the potential weight tensor format is a TT.
A Support Tensor Train Machine
2019 International Joint Conference on Neural Networks (IJCNN)
2019 International Joint Conference on Neural Networks (IJCNN)
Computer Science Mathematics