Robustness of High-Dimensional Data Mining

0432406 - ÚI 2015 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Kalina, Jan - Duintjer Tebbens, Jurjen - Schlenker, Anna
Robustness of High-Dimensional Data Mining.
ITAT 2014. Information Technologies - Applications and Theory. Part II. Prague: Institute of Computer Science AS CR, 2014 - (Kůrková, V.; Bajer, L.; Peška, L.; Vojtáš, R.; Holeňa, M.; Nehéz, M.), s. 53-60. ISBN 978-80-87136-19-5.
[ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./. Demänovská dolina (SK), 25.09.2014-29.09.2014]
Grant CEP: GA ČR GA13-17187S; GA ČR GA13-06684S
Grant ostatní: GA UK(CZ) 264513; CESNET Development Fund(CZ) 494/2013
Institucionální podpora: RVO:67985807
Klíčová slova: classification analysis * robust estimation * high-dimensional data
Kód oboru RIV: IN - Informatika

Standard data mining procedures are sensitive to the presence of outlying measurements in the data. This work has the aim to propose robust versions of some existing data mining procedures, i.e. methods resistant to outliers. In the area of classification analysis, we propose a new robust method based on a regularized version of the minimum weighted covariance determinant estimator. The method is suitable for data with the number of variables exceeding the number of observations. The method is based on implicit weights assigned to individual observations. Our approach is a unique attempt to combine regularization and high robustness, allowing to downweight outlying high-dimensional observations. Classification performance of new methods and some ideas concerning classification analysis of high-dimensional data are illustrated on real raw data as well as on data contaminated by severe outliers.
Trvalý link: http://hdl.handle.net/11104/0236770