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Highly Robust Classification: A Regularized Approach for Omics Data

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F16%3A00457036" target="_blank" >RIV/67985807:_____/16:00457036 - isvavai.cz</a>

  • Alternative codes found

    RIV/00023752:_____/16:43915358

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Highly Robust Classification: A Regularized Approach for Omics Data

  • Original language description

    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 perform

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    BIOSTEC 2016 - BIOINFORMATICS

  • ISBN

    978-989-758-170-0

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    17-26

  • Publisher name

    Scitepress

  • Place of publication

    Lisbon

  • Event location

    Rome

  • Event date

    Feb 21, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article