A Linear Support Higher-Order Tensor Machine for Classification

作者：Hao, Zhifeng^*; He, Lifang; Chen, Bingqian; Yang, Xiaowei

来源：IEEE Transactions on Image Processing, 2013, 22(7): 2911-2920.

DOI：10.1109/TIP.2013.2253485

• Higher-order tensor
• support tensor machine (STM)
• support vector machine (SVM)
• tensor classification
• tensor rank-one decomposition

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.

• 出版日期2013-7
• 单位广东工业大学; 华南理工大学; 吉林大学