Entropy-Randomized Method for the Reconstruction of Missing Data
© 2021, Pleiades Publishing, Ltd.Abstract: The article deals with the problem of reconstructing missing data in data collections formachine learning problems. We propose a new randomized method for missing datareconstruction based on the technology of entropy-robust estimation and generation of ensemblesof random variables. The method is similar to the use of an auxiliary regression to reconstructmissing values, but unlike the latter, no additional constraints are imposed on the likelihoodfunction of errors in the sample in the case of entropy estimation and small amounts of data arepermissible; this becomes extremely relevant in problems where the amount of data for training islimited and the omissions are not systematic. The proposed method is used to reconstruct missingdata on the areas of thermokarst lakes in the Arctic zone of the Russian Federation as measuredfrom satellite images.