文献类型：会议论文

英文题名：A linear support higher order tensor domain description for one-class classification

作者：Chen, Yanyan[1,2]; Wang, Kuaini[3]; Zhong, Ping[1]

第一作者：陈艳燕;Chen, Yanyan

通讯作者：Zhong, Ping

机构：[1] College of Science, China Agricultural University, Beijing, 100083, China; [2] College of Applied Science and Technology, Beijing Union University, Beijing, China; [3] College of Science, Xi'An Shiyou University, Xi'an, China

第一机构：College of Science, China Agricultural University, Beijing, 100083, China

语种：英文

外文关键词：Classification (of information)

摘要：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. ? 2018 - IOS Press and the authors. All rights reserved.