One-Class Support Tensor Machine

In fault diagnosis, face recognition, network anomaly detection, text classification and many other fields, we often encounter one-class classification problems. The traditional vector-based one-class classification algorithms represented by One-Class Support Vector Machine (OCSVM) have limitations when tensor is considered as input data. This work addresses one-class classification problem with tensor-based maximal margin classification paradigm. To this end, we formulate the One-Class Support Tensor Machine (OCSTM), which separates most samples of interested class from the origin in the tensor space, with maximal margin. The benefits of the proposed algorithm are twofold. First, the use of direct tensor representation helps to retain the data topology more efficiently. The second benefit is that tensor representation can greatly reduce the number of parameters. It helps overcome the overfitting problem caused mostly by vector-based algorithms and especially suits for high dimensional and small sample size problem. To solve the corresponding optimization problem in OCSTM, the alternating projection method is implemented, for it is simplified by solving a typical OCSVM optimization problem at each iteration. The efficiency of the proposed method is illustrated on both vector and tensor datasets. The experimental results indicate the validity of the new method.