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Authors: Jan Kalina and Jaroslav Hlinka

Affiliation: Institute of Computer Science CAS and National Institute of Mental Health, Czech Republic

Keyword(s): High-dimensional Data, Classification Analysis, Robustness, Outliers, Regularization.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning

Abstract: Various regularized approaches to linear discriminant analysis suffer from sensitivity to the presence of outlying measurements in the data. This work has the aim to propose new versions of regularized linear discriminant analysis suitable for high-dimensional data contaminated by outliers. We use principles of robust statistics to propose classification methods suitable for data with the number of variables exceeding the number of observations. Particularly, we propose two robust regularized versions of linear discriminant analysis, which have a high breakdown point. For this purpose, we propose a regularized version of the minimum weighted covariance determinant estimator, which is one of highly robust estimators of multivariate location and scatter. It assigns implicit weights to individual observations and represents a unique attempt to combine regularization and high robustness. Algorithms for the efficient computation of the new classification methods are proposed and the perfo rmance of these methods is illustrated on real data sets. (More)

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Paper citation in several formats:
Kalina, J. and Hlinka, J. (2016). Highly Robust Classification: A Regularized Approach for Omics Data. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - BIOINFORMATICS; ISBN 978-989-758-170-0; ISSN 2184-4305, SciTePress, pages 17-26. DOI: 10.5220/0005623500170026

@conference{bioinformatics16,
author={Jan Kalina and Jaroslav Hlinka},
title={Highly Robust Classification: A Regularized Approach for Omics Data},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - BIOINFORMATICS},
year={2016},
pages={17-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005623500170026},
isbn={978-989-758-170-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - BIOINFORMATICS
TI - Highly Robust Classification: A Regularized Approach for Omics Data
SN - 978-989-758-170-0
IS - 2184-4305
AU - Kalina, J.
AU - Hlinka, J.
PY - 2016
SP - 17
EP - 26
DO - 10.5220/0005623500170026
PB - SciTePress

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