最小二乘半监督支持张量机学习算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201607007

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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.

Key words： Semi-supervised Learning Support Tensor Machine(STM) Least Squares Support Tensor Machine

Received: 02 March 2016

ZTFLH:

TP 391

About author:: LU Chengtao(Corresponding author),born in 1992,master student. His research interests include machine learning and pattern recognition.LI Fanzhang, born in 1964, master, professor. His research interests include Lie Group machine learning, data mining and dynamic fuzzy logicZHANG Li, born in 1975, Ph.D., professor. Her research interests include pattern recognition, machine learning and data mining.ZHANG Zhao, born in 1984, Ph.D., associate professor. His research interests include pattern recognition, machine learning, data mining and computer vision.

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	LU Chengtao
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LU Chengtao,LI Fanzhang,ZHANG Li等. Least Squares Semi-supervised Support Tensor Machine[J]. , 2016, 29(7): 633-640.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201607007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I7/633

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