Abstract
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.
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Acknowledgments
Preliminary results were first presented at the BIOSTEC/BIOINFORMATICS 2016 conference (21–23 February 2016 in Rome), where they were published in the proceedings.
The work was supported by the project Nr. LO1611 with a financial support from the MEYS under the NPU I program. The work of J. Kalina was financially supported by the Neuron Fund for Support of Science. The work of J. Hlinka was supported by the Czech Science Foundation project No. 13-23940S.
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Kalina, J., Hlinka, J. (2017). Implicitly Weighted Robust Classification Applied to Brain Activity Research. In: Fred, A., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2016. Communications in Computer and Information Science, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-319-54717-6_6
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DOI: https://doi.org/10.1007/978-3-319-54717-6_6
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