统计与决策2023，Vol.39Issue(23)：40-45,6.DOI:10.13546/j.cnki.tjyjc.2023.23.007
融合类信息的函数型矩阵填充方法与应用
Functional Matrix Completion Method With Class Information and Its Application
摘要
Abstract
The complete acquisition of real-time vehicle flow,average lane occupancy and other traffic monitoring data is an important basis for the construction of intelligent transportation systems and the improvement of traffic management efficiency.This paper proposes a Functional Matrix Completion Method with Class Information(CFMC).In the framework of functional data analysis,a functional matrix completion model is constructed based on nonnegative matrix factorization.On this basis,the sample class information is introduced by clustering division;the missing values is imputed by intra-class sample correlation,and the fi-nal imputation values is calculated by dynamic weight reweighting based on self-weighted ensemble learning algorithm.The impu-tation experiment is carried out on the public transport data set PeMS,and the results show that when the missing rate is 15%～70%,compared with K-nearest neighbor algorithm,MICE,PACE and other 10 imputation methods,the root mean square error(RMSE),mean absolute error(MAE)and mean absolute percentage error(MAPE)of CFMC method are reduced by 10.75%～81.69%,0.34%～84.48%and 12.5%～81.08%,respectively,with the time consumption controllable.The proposed CFMC method has high imputation precision,greatly robustness,able to guarantee the effectiveness and accuracy of imputation.关键词
函数型数据分析/非负矩阵分解/矩阵填充/交通流量/缺失插补Key words
functional data analysis/nonnegative matrix factorization/matrix completion/traffic flow/missing imputation分类
数理科学引用本文复制引用
高海燕,马文娟,薛娇..融合类信息的函数型矩阵填充方法与应用[J].统计与决策,2023,39(23):40-45,6.基金项目
国家社会科学基金资助项目(19XTJ002) （19XTJ002）
甘肃省自然科学基金资助项目(23JRRA1186) （23JRRA1186）
甘肃省优秀研究生"创新之星"项目(2023CXZX-703) （2023CXZX-703）
兰州财经大学科研项目(Lzufe2023C-005) （Lzufe2023C-005）
