Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (8): 52-57.doi: 10.11896/jsjkx.220500277

• Database & Big Data & Data Science • Previous Articles     Next Articles

Data Completion of Air Quality Index Based on Multi-dimensional Sparse Representation

CAI Qiquan1, LU Juhong1, YU Zhiyong1,2, HUANG Fangwan1,2   

  1. 1 College of Computer and Data Science,Fuzhou University,Fuzhou 350108,China
    2 Fujian Key Laboratory of Network Computing and Intelligent Information Processing(Fuzhou University),Fuzhou 350108,China
  • Received:2022-05-30 Revised:2022-11-04 Online:2023-08-15 Published:2023-08-02
  • About author:CAI Qiquan,born in 1997,undergra-duate.His main research interests include data completion and so on.
    HUANG Fangwan,born in 1980,postgraduate,senior lecturer,is a member of China Computer Federation.Her main research interests include computa-tional intelligence,machine learning and big data analysis.
  • Supported by:
    National Natural Science Foundation of China(61772136),Fujian Provincial Guiding Project(2020H0008) and Educational Research Project for Young and Middle-aged Teachers in Fujian Province(JAT210007).

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Abstract: In recent years,air pollution has become increasingly serious and become one of the risk factors affecting people's health.The air quality index(AQI) can provide the government with the laws of atmospheric environment changes,and can also be used for air pollution control.However,the data is inevitably missing in the process of collection,which leads to the difficulty of data mining.However,given the poor performance of existing completion methods under a high miss rate,this paper transforms the missing-matrix-completion problem into a sparse-matrix-reconstruction problem and designs a data completion method based on multi-dimensional sparse representation.The method first uses the training data to simulate various random missing cases for over-complete dictionary learning.Then,the sparse representation of the matrix with missing values is obtained by using the upper part of the learned dictionary.Finally,the sparse representation is combined with the lower part of the dictionary to obtain the reconstructed estimation matrix.Experimental results show that the proposed algorithm is superior to the traditional matrix method in the completion of multi-dimensional time series of AQI,especially in the case of serious missing.

Key words: Air quality index, Missing data, Matrix completion, Dictionary learning, Multi-dimensional sparse representation

CLC Number: 

  • TP391
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