Affiliations: [a] College of Science, China Agricultural University, Beijing, China
| [b] College of Applied Science and Technology, Beijing Union University, Beijing, China
| [c] College of Science, Xi’an Shiyou University, Xi’an, China
Correspondence:
[*]
Corresponding author. Ping Zhong, College of Science, China Agricultural University, Beijing 100083, China. E-mail: zping@cau.edu.cn
Abstract: One-class classification is an important problem encountered in a lot of applications. The datasets extracted from the real-world problems are often represented as tensors. The classical support vector domain description (SVDD) for one-class classification problems cannot work directly since its inputs are vectors. This paper develops a linear tensor-based algorithm named as Linear Support Tensor Domain Description (LSTDD) to find a closed hypersphere with the minimal volume in the tensor space which can contain almost entirely of the target samples. LSTDD can keep data topology and make the parameters need to be estimated less, and it is more suitable for learning the high dimensional and small sample size problem. Firstly, we detail the LSTDD model with 2nd-order tensors, and then extend it to the higher order tensors. It has been shown by experiments on the real-world datasets that LSTDD is a promising method for handling one-class classification problems with both 2nd-order and higher order tensor inputs.
Keywords: One-class classification, support vector domain description, support tensor
machine, support tensor domain description
