Highly Robust Classification: A Regularized Approach for Omics Data

Jan Kalina, Jaroslav Hlinka

2016

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 performance of these methods is illustrated on real data sets.

Paper Citation

in Harvard Style

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 - Volume 3: BIOINFORMATICS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 17-26. DOI: 10.5220/0005623500170026

in Bibtex Style

@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 - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)},
year={2016},
pages={17-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005623500170026},
isbn={978-989-758-170-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)
TI - Highly Robust Classification: A Regularized Approach for Omics Data
SN - 978-989-758-170-0
AU - Kalina J.
AU - Hlinka J.
PY - 2016
SP - 17
EP - 26
DO - 10.5220/0005623500170026