Název: Robust Regularized Discriminant Analysis Based on Implicit Weighting
Autoři: Kalina, Jan ; Hlinka, Jaroslav
Typ dokumentu: Výzkumné zprávy
Rok: 2016
Jazyk: eng
Edice: Technical Report, svazek: V-1241
Abstrakt: In bioinformatics, regularized linear discriminant analysis is commonly used as a tool for supervised classification problems tailormade 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 highdimensional 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.
Klíčová slova: classification analysis; high-dimensional data; outliers; regularization; robustness
Číslo projektu: ED2.1.00/03.0078, GA13-23940S (CEP)
Poskytovatel projektu: GA MŠk, GA ČR
Práva: Dílo je chráněno podle autorského zákona č. 121/2000 Sb.

Instituce: Ústav informatiky AV ČR (web)
Původní záznam: http://hdl.handle.net/11104/0264994

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-262425


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