loading
Documents

Research.Publish.Connect.

Paper

Authors: Jan Kalina and Jaroslav Hlinka

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

ISBN: 978-989-758-170-0

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 perfor mance of these methods is illustrated on real data sets. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.159.44.54

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.