文献类型：期刊文献

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, P[1]

机构：[1]China Agr Univ, Coll Sci, Beijing 100083, Peoples R China;[2]Beijing Union Univ, Coll Appl Sci & Technol, Beijing, Peoples R China;[3]Xian Shiyou Univ, Coll Sci, Xian, Shaanxi, Peoples R China

第一机构：China Agr Univ, Coll Sci, Beijing 100083, Peoples R China

通讯机构：[1]corresponding author), China Agr Univ, Coll Sci, Beijing 100083, Peoples R China.

年份：2018

卷号：34

期号：6

起止页码：4237-4247

外文期刊名：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000436432400070)】；

基金：The work is supported by the National Natural Science Foundation of China (Nos. 11171346, 11626186), New Start Academic Research Projects of Beijing Union University No. Zk10201513, and Xi'an Shiyou University Youth Science and Technology Innovation Fund Project No. 2016BS17.

语种：英文

外文关键词：One-class classification; support vector domain description; support tensor machine; support tensor domain description

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.