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Information extraction from high-dimensional data represents an important problem in current\udapplications in management or econometrics. An important problem from a practical point of view\udis the sensitivity of machine learning methods with respect to the presence of outlying data values,\udwhile numerical stability represents another important aspect of data mining from high-dimensional\uddata. This paper gives an overview of various types of data mining, discusses their suitability for\udhigh-dimensional data and critically discusses their properties from the robustness point of view,\udwhile we explain that the robustness itself is perceived differently in different contexts.Moreover, we\udinvestigate properties of a robust nonlinear regression estimator of Kalina (2013)

10.5937/sjm9-5520 = On robust information extraction from high-dimensional data