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
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.
余可鸣，韩乐，杨晓伟. 弹球支持张量机分类器*[J]. 模式识别与人工智能, 2016, 29(7): 598-607.
YU Keming, HAN Le, YANG Xiaowei. Support Tensor Machine Classifier with Pinball Loss. , 2016, 29(7): 598-607.
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2016, Vol. 29 
