关键词: 在线学习, 支持张量机, 支持向量机, 张量秩一分解

Abstract: Based on the stochastic gradient descent method, this paper proposes online support tensor machine (OSTM) algorithm for tensor classification. In OSTM, its input patterns are tensors which are collected one by one in a sequence. In order to maintain the natural structure and correlation in the original tensor data, and reduce training time and memory space, OSTM algorithm applies tensor rank-one decomposition to replace the original tensor and assist tensor inner computation. The experiments on thirteen tensor datasets show that compared with the online support vector machine, OSTM can provide a significant improvement in training speed with comparable test accuracy, especially for higher-order tensors.

Key words: online learning, support tensor machine, support vector machine, tensor rank-one decomposition