摘要
机器学习、模式识别、数据挖掘等领域中的输入模式常常是高阶张量.文中首先从向量模式推广到张量模式,提出弹球支持张量机模型.然后给出求解弹球支持张量机模型的序贯最小优化算法(SMO).为了保持张量的自然结构信息,同时加速训练过程,采用张量的秩-1分解代替原始张量计算张量内积.在向量数据和张量数据上进行的大量实验表明:对于向量数据,相比经典的积极集法,SMO的计算速度更快;对于张量数据,相比弹球支持向量机,弹球支持张量机具有更快的训练速度和更好的泛化能力.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2016年第7期598-607,共10页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.11501219
61273295)资助~~
