Implicitly Weighted Robust Classification Applied to Brain Activity Research

0473143 - ÚI 2018 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
Kalina, Jan - Hlinka, Jaroslav
Implicitly Weighted Robust Classification Applied to Brain Activity Research.
Biomedical Engineering Systems and Technologies. Cham: Springer, 2017 - (Fred, A.; Gamboa, H.), s. 87-107. Communications in Computer and Information Science, 690. ISBN 978-3-319-54716-9. ISSN 1865-0929.
[BIOSTEC 2016 International Joint Conference /9./. Rome (IT), 21.02.2016-23.02.2016]
Grant CEP: GA ČR GA13-23940S
Grant ostatní: GA MŠk(CZ) LO1611; Nadační fond na podporu vědy(CZ) Neuron
Institucionální podpora: RVO:67985807
Klíčová slova: high-dimensional data * classification analysis * robustness * outliers * regularization
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

In bioinformatics, regularized linear discriminant analysis is commonly used as a tool for supervised classification problems tailor-made for high-dimensional data with the number of variables exceeding the number of observations. However, its various available versions are too vulnerable to the presence of outlying measurements in the data. In this paper, we exploit principles of robust statistics to propose new versions of regularized linear discriminant analysis suitable for high-dimensional data contaminated by (more or less) severe outliers. The work exploits a regularized version of the minimum weighted covariance determinant estimator, which is one of highly robust estimators of multivariate location and scatter. The performance of the novel classification methods is illustrated on real data sets with a detailed analysis of data from brain activity research.
Trvalý link: http://hdl.handle.net/11104/0270309