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Properties of the Weighted and Robust Implicitly Weighted Correlation Coefficients

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    SYSNO ASEP0577080
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleProperties of the Weighted and Robust Implicitly Weighted Correlation Coefficients
    Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID
    Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
    Source TitleArtificial Neural Networks and Machine Learning – ICANN 2023. Proceedings, Part IX. - Cham : Springer, 2023 / Iliadis L. ; Papaleonidas A. ; A P. ; Jayne C. - ISSN 0302-9743 - ISBN 978-3-031-44200-1
    Pagess. 200-212
    Number of pages13 s.
    Publication formPrint - P
    ActionICANN 2023: International Conference on Artificial Neural Networks /32./
    Event date26.09.2023 - 29.09.2023
    VEvent locationHeraklion
    CountryGR - Greece
    Event typeWRD
    Languageeng - English
    CountryCH - Switzerland
    KeywordsCorrelation coefficient ; Outliers ; Robustness ; Image analysis ; Approximate computing
    OECD categoryComputer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    R&D ProjectsGA22-02067S GA ČR - Czech Science Foundation (CSF)
    Institutional supportUIVT-O - RVO:67985807
    UT WOS001157308600017
    EID SCOPUS85174632407
    DOI10.1007/978-3-031-44201-8_17
    AnnotationPearson product-moment correlation coefficient represents a fundamental measure of similarity between two data vectors. In various applications, it is meaningful to consider its weighted version known as the weighted Pearson correlation coefficient. Its properties are studied in this theoretical paper - these include the robustness to rounding, as it is an important issue in approximate neurocomputing, or specific robustness properties for the context of template matching in image analysis. For a highly robust correlation coefficient inspired by the least weighted estimator, properties are derived and novel hypothesis tests are proposed. This robust measure is recommendable particularly for data contaminated by outliers (not only) in the context of image analysis.
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Year of Publishing2024
    Electronic addresshttps://dx.doi.org/10.1007/978-3-031-44201-8_17
Number of the records: 1  

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