A Low-Rank Approximation-Based Transductive Support Tensor Machine for Semisupervised Classification

In the fields of machine learning, pattern recognition, image processing, and computer vision, the data are usually represented by the tensors. For the semisupervised tensor classification, the existing transductive support tensor machine (TSTM) needs to resort to iterative technique, which is very time-consuming. In order to overcome this shortcoming, in this paper, we extend the concave-convex procedure-based transductive support vector machine (CCCP-TSVM) to the tensor patterns and propose a low-rank approximation-based TSTM, in which the tensor rank-one decomposition is used to compute the inner product of the tensors. Theoretically, concave-convex procedure-based TSTM (CCCP-TSTM) is an extension of the linear CCCP-TSVM to tensor patterns. When the input patterns are vectors, CCCP-TSTM degenerates into the linear CCCP-TSVM. A set of experiments is conducted on 23 semisupervised classification tasks, which are generated from seven second-order face data sets, three third-order gait data sets, and two third-order image data sets, to illustrate the performance of the CCCP-TSTM. The results show that compared with CCCP-TSVM and TSTM, CCCP-TSTM provides significant performance gain in terms of test accuracy and training speed.