On Robust Information Extraction from High-Dimensional Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00427963" target="_blank" >RIV/67985807:_____/14:00427963 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.5937/sjm9-5520" target="_blank" >http://dx.doi.org/10.5937/sjm9-5520</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5937/sjm9-5520" target="_blank" >10.5937/sjm9-5520</a>

Result language
angličtina
Original language name
On Robust Information Extraction from High-Dimensional Data
Original language description
Information extraction from high-dimensional data represents an important problem in current applications in management or econometrics. An important problem from a practical point of view is the sensitivity of machine learning methods with respect to the presence of outlying data values, while numerical stability represents another important aspect of data mining from high-dimensional data. This paper gives an overview of various types of data mining, discusses their suitability for high-dimensional data and critically discusses their properties from the robustness point of view, while we explain that the robustness itself is perceived differently in different contexts. Moreover, we investigate properties of a robust nonlinear regression estimator ofKalina (2013).
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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