文献类型：期刊文献

英文题名：One-class support higher order tensor machine classifier

作者：Chen, Yanyan[1,2];Lu, Liyun[1];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

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：2017

卷号：47

期号：4

起止页码：1022-1030

外文期刊名：APPLIED INTELLIGENCE

收录：;EI(收录号：20171903653632);Scopus(收录号：2-s2.0-85019019702);WOS:【SCI-EXPANDED(收录号:WOS:000414779600003)】；

基金：The work is supported by the National Natural Science Foundation of China No. 11171346, the Chinese Universities Scientific Fund No. 2016LX002, and the "New Start" Academic Research Projects of Beijing Union University No. Zk10201513.

语种：英文

外文关键词：Support vector machine; One-class support vector machine; Support tensor machine; Higher order tensor; One-class classification

摘要：One-class classification problems have been widely encountered in the fields that the negative class patterns are difficult to be collected, and the one-class support vector machine is one of the popular algorithms for solving them. However, one-class support vector machine is a vector-based learning algorithm, and it cannot work directly when the input pattern is a tensor. This paper proposes a tensor-based maximum margin classifier for one-class classification problems, and develops a One-Class Support Higher Order Tensor Machine (HO-OCSTM) which can separate most of the target patterns from the origin with the maximum margin in the higher order tensor space. HO-OCSTM directly employs the higher order tensors as the input patterns, and it is more proper for small sample study. Moreover, the direct use of tensor representation has the advantage of retaining the structural information of data, which helps improve the generalization ability of the proposed algorithm. We implement HO-OCSTM by the alternating projection method and solve a convex quadratic programming similar to the standard one-class support vector machine algorithm at each iteration. The experimental results have shown the high recognition accuracy of the proposed method.