Original title: Robust Regularized Discriminant Analysis Based on Implicit Weighting
Authors: Kalina, Jan ; Hlinka, Jaroslav
Document type: Research reports
Year: 2016
Language: eng
Series: Technical Report, volume: V-1241
Abstract: 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.
Keywords: classification analysis; high-dimensional data; outliers; regularization; robustness
Project no.: ED2.1.00/03.0078, GA13-23940S (CEP)
Funding provider: GA MŠk, GA ČR
Rights: This work is protected under the Copyright Act No. 121/2000 Coll.

Institution: Institute of Computer Science AS ČR (web)
Original record: http://hdl.handle.net/11104/0264994

Permalink: http://www.nusl.cz/ntk/nusl-262425


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