Robust Regularized Discriminant Analysis Based on Implicit Weighting

0466759 - ÚI 2017 CZ eng V - Research Report
Kalina, Jan - Hlinka, Jaroslav
Robust Regularized Discriminant Analysis Based on Implicit Weighting.
Prague: ICS CAS, 2016. 21 s. Technical Report, V-1241.
R&D Projects: GA ČR GA13-23940S
Grant - others:GA MŠk(CZ) ED2.1.00/03.0078
Institutional support: RVO:67985807
Keywords : high-dimensional data * classification analysis * robustness * outliers * regularization
Subject RIV: BB - Applied Statistics, Operational Research

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.
Permanent Link: http://hdl.handle.net/11104/0264994