Linear One-Class Support Tensor Machine

One-class support vector machine is an important and efficient classifier which is used in the situation that only one class of data is available, and the other is too expensive or difficult to collect. It uses vector as input data, and trains a linear or nonlinear decision function in vector space. However, there is reason to consider data as tensor. Tensor representation can make use of the structural information present in the data, which cannot be handled by the traditional vector based classifier. The significant benefit of using tensor as input is the reduction of the number of decision parameters, which can avoid the overfitting problems and especially suitable for small sample and large dimension cases. In this paper we have proposed a tensor based one-class classification algorithm named linear one-class support tensor machine. It aims to find a hyperplane in tensor space with maximal margin from the origin that contains almost all the data of the target class. We demonstrate the performance of the new tensor based classifier on several publicly available datasets in comparison with the standard linear one-class support vector machine. The experimental results indicate the validity and advantage of our tensor based classifier.