| Kurzfassung
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In this thesis, the idea of finding robust subspaces with help of an iterative process is being discussed. The process firstly aims for subspaces where hiding outliers is feasible. Subsequently, the subspaces used in the first part are being adjusted. In doing so, the convergence of this iterative process can reveal valuable insights in systems where the existence of hidden outliers poses a high risk (e.g. power station). The main part of this thesis will deal with the aspect of hiding outliers in high dimensional data spaces and the challenges resulting from such spaces.
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