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| Support Tensor Machine Classifier with Pinball Loss |
| YU Keming1, HAN Le1, YANG Xiaowei2 |
1.School of Mathematics, South China University of Technology, Guangzhou 510640
2.School of Software Engineering, South China University of Technology, Guangzhou 510006 |
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Abstract The input patterns are usually high-order tensors in the fields of machine learning, pattern recognition, data mining, etc. In this paper, the pin-support vector machine is firstly extended from vector to tensor and the support tensor machine (STM) classifier with pinball loss(pin-STM) is proposed. Then, a sequential minimal optimization (SMO) algorithm is designed to solve this model. To maintain the nature structure of tensor and speed up the training procedure, the rank-one decomposition of tensor is used to substitute the original tensor to compute the inner products of tensors. The experimental results on vector datasets and tensor datasets show that SMO is faster than the classical active-set method for vector data. Compared with pin-SVM, the pin-STM has higher training speed and better generalized performance for tensor data.
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Received: 01 July 2015
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| About author:: YU Keming, born in 1991, master student. Her research interests include machine learning and tensor analysis.HAN Le, born in 1977, Ph.D., associate professor. Her research interests include matrix optimization and machine lear-ning.YANG Xiaowei(Corresponding author), born in 1969, Ph.D., professor. His research interests include machine learning, pattern recognition, data mining and tensor analysis.) |
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2016, Vol. 29 
