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中文摘要：
支持张量机（STM）受限于迭代操作，训练时间较长．针对这一缺点，改进STM的目标规划，将训练过程由解决一组二次规划改为计算线性方程组，并引入直推式的思想解决半监督问题，提出最小二乘半监督支持张量机学习算法．在人脸识别和时间序列分类上对比文中算法与传统算法，实验证明文中算法不仅减少运算时间，而且提高识别率．
英文摘要：
Support tensor machine has a high computational complexity due to the iterative procedure. To overcome the shortcoming, the optimization is modified , the model is trained by solving a set of linear equations instead of solving a quadratic program problem. Additionally, transductive method is used to solve the semi-supervised problem, least squares semi-supervised support tensor machine is proposed. Some experiments on face recognition and time series classification are conducted to compare the proposed algorithm with the traditional algorithms. The results show that the proposed algorithm reduces the computation time and improves the recognition rate.
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