A Linear Support Higher-Order Tensor Machine for Classification
A Linear Support Higher-Order Tensor Machine for Classification
复制标题
用于分类的线性支撑高阶张量机
DOI：
10.1109/tip.2013.2253485
复制
发表时间：
2013-07-01
影响因子：
10.6
通讯作者：
Yang, Xiaowei
中科院分区：
文献类型：
--
作者：
Hao, Zhifeng;He, Lifang;Yang, Xiaowei
人们对开发更有效的张量分类学习机的兴趣越来越大。目前，大多数现有的学习机，如支持张量机（STM），涉及非凸优化问题，需要诉诸迭代技术。显然，这是非常耗时的，并可能遭受局部最小值。为了克服这两个缺点，本文提出了一种新的线性支持高阶张量机（SHTM），它集成了线性C-支持向量机（C-SVM）和张量秩一分解的优点。从理论上讲，SHTM是线性C-SVM到张量模式的扩展。当输入模式为向量时，SHTM退化为标准C-SVM。一组实验进行了九个二阶人脸识别数据集和三个三阶步态识别数据集，以说明所提出的SHTM的性能。统计检验表明，与STM和基于RBF核的C-SVM相比，SHTM在测试精度和训练速度方面都有显著的提高，尤其是在高阶张量的情况下。
There has been growing interest in developing more effective learning machines for tensor classification. At present, most of the existing learning machines, such as support tensor machine (STM), involve nonconvex optimization problems and need to resort to iterative techniques. Obviously, it is very time-consuming and may suffer from local minima. In order to overcome these two shortcomings, in this paper, we present a novel linear support higher-order tensor machine (SHTM) which integrates the merits of linear C-support vector machine (C-SVM) and tensor rank-one decomposition. Theoretically, SHTM is an extension of the linear C-SVM to tensor patterns. When the input patterns are vectors, SHTM degenerates into the standard C-SVM. A set of experiments is conducted on nine second-order face recognition datasets and three third-order gait recognition datasets to illustrate the performance of the proposed SHTM. The statistic test shows that compared with STM and C-SVM with the RBF kernel, SHTM provides significant performance gain in terms of test accuracy and training speed, especially in the case of higher-order tensors.