Abstract
Very often analyzed data are contaminated with outliers which are due to some erroneous recording. After sorting out the suspected erroneous observations and coming to the conclusion that they are really erroneous, we would like to adjust them to the proper values.
In the paper we propose two methods (called Plug-in and Procrustes) which taking into account the interdependencies between the variables provided in a robust covariance matrix S* — permit to reconstruct the data matrix in such a way, that its covariance matrix is exactly equal to the given robust covariance matrix S*. We call this process backprojection through the robust covariance matrix. The proposed method of backprojection is shown on four benchmark data sets.
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Bartkowiak, A., Ziętak, K. (2002). Backprojection of data vectors using a given covariance matrix. In: Sołdek, J., Pejaś, J. (eds) Advanced Computer Systems. The Springer International Series in Engineering and Computer Science, vol 664. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8530-9_2
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DOI: https://doi.org/10.1007/978-1-4419-8530-9_2
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