基于流形正则化的多元时间序列半监督回归  

Manifold regularization based semi-supervised regression on multivariate time series

作　　者：赵志凯[1] 钱建生[2] 程健 李小斌[1] 

机构地区：[1]中国矿业大学计算机科学与技术学院,江苏徐州221116 [2]中国矿业大学信息与电气工程学院,江苏徐州221116

出　　处：《中国矿业大学学报》2011年第3期492-498,共7页Journal of China University of Mining & Technology

基　　金：国家高技术研究发展计划(863)项目(2008AA062200);中国博士后科学基金项目(20090460328)

摘　　要：针对多元时间序列半监督回归只考虑样本间空间关系信息而忽略了样本间时域信息的问题,提出了一种考虑样本间时域信息的半监督回归算法(ST-LapRLSR).在时域光滑性假设下,构造了一种能更好地反映样本间内蕴几何结构的正则化项.在建立图拉普拉斯的过程中,将样本点间的时序关系引入到边的权重计算中,并在流形正则化框架下利用该正则化项进行半监督回归,最后通过表示定理进行求解.在公共数据集和煤矿多传感器数据上进行了实验,结果表明:在多元时间序列半监督回归中,与只考虑样本空间关系信息的最小二乘正则化算法(LapRLSR)相比,ST-LapRLSR能同时利用样本的时空信息,准确率得到了提高.Traditional semi-supervised regression on multivariate time series only takes account of the spatial information of samples,and the temporal information is always neglected.To solve the problem,a semi-supervised regression algorithm ST-LapRLSR is proposed which takes account of the temporal information of samples.For time series an assumption of temporal smooth is proposed,and based on this assumption,a regularization item that could reflect more underline information of samples is constructed.During constructing graph Laplacian,the temporal relation of samples is used in computing edge weight.Semi-supervised regression under manifold regularization framework using the proposed regularization item is carried out,and solved by the Representer theorem.The experiments take on public dataset and multivariate sensor time series data of mine,and the results show that,in semi-supervised regression on multivariate time series,ST-LapRLSR which uses temporal and spatial information of samples simultaneously achieves better accuracy contrast to LapRLSR which only considers the spatial information of samples.

关 键 词：流形学习 正则化 半监督回归 多元时间序列 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]