  There has been growing interest in extending traditional vector-based machine
learning techniques to their tensor forms. An example is the support tensor
machine (STM) that utilizes a rank-one tensor to capture the data structure,
thereby alleviating the overfitting and curse of dimensionality problems in the
conventional support vector machine (SVM). However, the expressive power of a
rank-one tensor is restrictive for many real-world data. To overcome this
limitation, we introduce a support tensor train machine (STTM) by replacing the
rank-one tensor in an STM with a tensor train. Experiments validate and confirm
the superiority of an STTM over the SVM and STM.
