Some Imputation Algorithms for Restoration of Missing Data

  • Vladimir Ryazanov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


The problem of reconstructing the feature values in samples of objects given in terms of numerical features is considered. The three approaches, not involving the use of probability models and a priori information, are considered. The first approach is based on the organization of the iterative procedure for successive elaboration of missing values of attributes. In this case, the analysis of local information for each object with missing data is fulfilled. The second approach is based on solving an optimization problem. We calculate such previously unknown feature values for which there is maximum correspondence of metric relations between objects in subspaces of known partial values and found full descriptions. The third approach is based on solving a series of recognition tasks for each missing value. Comparisons of these approaches on simulated and real problems are presented.


missing data imputation feature pattern recognition feature values restoration 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vladimir Ryazanov
    • 1
  1. 1.Dorodnicyn Computing Centre of RASInstitution of Russian Academy of SciencesMoscowRussia

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