Journal of Guangxi Normal University(Natural Science Edition) ›› 2015, Vol. 33 ›› Issue (4): 55-62.doi: 10.16088/j.issn.1001-6600.2015.04.010

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KNN Imputation Algorithm Based on LPP and l2,1

SU Yi-juan1, SUN Ke2,3 , DENG Zhen-yun2,3, YIN Ke-jun2,3   

  1. 1.College of Computer and Information Engineering,Guangxi Teachers Education University,Nanning Guangxi 530023,China;
    2. College of Computer Science and Information Technology,Guangxi Normal University,Guilin Guangxi 541004, China;
    3.Guangxi Key Lab of Multi-sourceInformation Mining & Security, Guangxi Normal University,Guilin Guangxi 541004, China
  • Received:2015-03-16 Online:2015-12-25 Published:2018-09-21

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Abstract: Traditional KNN missing data filling algorithm does not utilize the correlation between the properties of samples, Neither considers but also does not consider to maintain the sample structures and removes noise samples. In this paper, a method of using training samples to reconstruct the test sample is proposed, which is used for the nearest neighbor missing data imputation. The method makes full use of the correlation between samples, uses the LPP (locality preserving projection) to maintain the data structure in the process of reconstruction, and uses l2,1 norm to remove noise samples. Simulation experiments on UCI data sets show that the proposed method has higher prediction accuracy than the traditional KNN algorithm and Entropy-KNN algorithm based on attribute information entropy.

Key words: missing data imputation, KNN, LPP, reconstruction

CLC Number: 

  • TP181
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