Air quality data restoration based on graph regularization multi-view functional matrix completion
Due to issues such as sensor malfunctions and data transmission,the collected air quality data often encounter challenges of sparsity and incompleteness.In order to effectively repair and reconstruct the missing parts of air quality data,a Graph Regularized Multi-view Functional Matrix Completion method(GRMFMC)is proposed.Firstly,this innovative method introduces a graph regularization approach that thoroughly takes into account the high-order neighborhood relationship within each pollutant's sample set,reducing information loss.Secondly,it utilizes the Hilbert-Schmidt Independence Criterion(HSIC)to discern complementary information among various pollutants,thereby improving imputation accuracy.Additionally,by integrating the principles of functional data analysis,the GRMFMC technique treats temporal air quality data as continuous functions,capitalizing on their inherent smoothness and correlation for high-precision data interpolation.Simulation imputations and empirical applications on real air quality datasets both demonstrate that the GRMFMC exhibits superior interpolation performance.In simulation imputations,the GRMFMC method reduces the imputation error by 56％～99％in RMSE and 46％～98％in NRMSE;in empirical applications,it reduces the error by 51％～99％in RMSE and 40％～98％in NRMSE.Furthermore,the GRMFMC method shows consistent robustness across different missing rate and pollutant categories,confirming its potential for generalization capability and practical value in professional settings.
functional data analysismulti-view learninggraph regularizationair quality datamatrix completiondata restoration
万方数据
维普
