文献类型：期刊文献

英文题名：A KERNEL-BASED SUPPORT TENSOR DATA DESCRIPTION FOR ONE-CLASS CLASSIFICATION

作者：Wang, Xue[1];Wang, Minghui[1];Wang, Kuaini[2];Chen, Yanyan[3]

第一作者：王新

通讯作者：Chen, YY[1]

机构：[1]Beijing Union Univ, Coll Appl Sci & Technol, Beijing 100101, Peoples R China;[2]Xian Shiyou Univ, Coll Sci, Xian 710065, Peoples R China;[3]Beijing Union Univ, Inst Math & Phys, Beijing 100101, Peoples R China

第一机构：北京联合大学应用科技学院

通讯机构：[1]corresponding author), Beijing Union Univ, Inst Math & Phys, Beijing 100101, Peoples R China.|[11417]北京联合大学;

年份：2023

卷号：85

期号：1

起止页码：197-208

外文期刊名：UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

收录：EI(收录号：20231313797952);Scopus(收录号：2-s2.0-85150663729);WOS:【ESCI(收录号:WOS:001032564800002)】；

基金：The work is supported by the National Natural Science Foundation of China under No. 61907033.

语种：英文

外文关键词：Support Tensor Data Description; Support Vector Domain Description; Kernel matrix; One-class classification

摘要：The issue of one-class classification has got a great deal of studies. However, the classical algorithms represented by Support Vector Data Description (SVDD) have restrictions when the input is not vector. Therefore, we present a nonlinear tensor-based data description that is named as Kernel-based Support Tensor Data Description (KSTDD). The basic thought of KSTDD is to seek for an enclosing hypersphere of smallest volume that contains most of target objects. KSTDD uses tensor as input, and it has the ability to keep more data topology. Meanwhile, the number of parameters that need to be estimated by KSTDD is reduced considerably, which makes KSTDD more fit for small-sample learning. KSTDD is iteratively solved, and the computation complexity and the convergence of the corresponding iterative algorithm are provided respectively. We prove that KSTDD is equivalent to One-Class Support Tensor Machine (OCSTM) for Gaussian-based kernel matrix. However, the two algorithms cannot be completely equivalent to each other since they are quite different for other kernel matrices. Therefore, we evaluate KSTDD with different kernel matrices including Gaussian-based kernel matrices and polynomial-based kernel matrices. Experiments have verified the efficiency of the KSTDD.