Abstract
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.
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Ryazanov, V. (2011). Some Imputation Algorithms for Restoration of Missing Data. In: San Martin, C., Kim, SW. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011. Lecture Notes in Computer Science, vol 7042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25085-9_44
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DOI: https://doi.org/10.1007/978-3-642-25085-9_44
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