Abstract：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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2016, Vol. 29 
