文献类型：期刊文献

英文题名：A nonlinear tensor-based machine learning algorithm for image classification

作者：Wang, Tingmei[1]; Chen, Yanyan[1]

第一作者：王廷梅

通讯作者：Chen, Yanyan

机构：[1] College of Applied Science and Technology, Beijing Union University, Beijing, 102200, China

第一机构：北京联合大学应用科技学院

年份：2019

卷号：33

期号：6

起止页码：475-481

外文期刊名：Revue d'Intelligence Artificielle

收录：EI(收录号：20201108289411);Scopus(收录号：2-s2.0-85081125101)

基金：The work is supported by The Research and Practice on The Through-Type Training Mode of High-End Technologies and Technical Skills Personnel of Beijing (Beijing Municipal Education Commission, Beijing, China, Grant No. 2018-54), The Famous Teacher of Beijing and The Academic Research Project of Beijing Union University (ZK50201902).

语种：英文

外文关键词：Image classification - Learning systems - Numerical methods - Support vector machines - Tensors

摘要：In recent year, the tensor theory has been frequently incorporated to machine learning, because of the various advantages of tensor-based machine learning over vector-based machining learning: the ability to preserve the spatiotemporal information, allowing full utilization of the data, and the suitability for solving high-dimensional problems with a small sample size. Considering the suitability of tensor algorithm for classical high-dimensional, small-sample problems, this paper probes into the nonlinear classification problem with tensor representation, and designs a tensor-based nonlinear classification algorithm, namely, the kernel-based STM (KSTM). The maximum margin principle was adopted for the classification by the KSTM: the two types of samples are separated by the decision hyperplane as far as possible in the tensor space. Through numerical experiments, it is proved that the KSTM achieved better classification accuracy than the linear method, especially for the high-dimensional problem with a small sample size. ? 2019 Lavoisier. All rights reserved.