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Properties of the Weighted and Robust Implicitly Weighted Correlation Coefficients
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SYSNO ASEP 0577080 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Properties of the Weighted and Robust Implicitly Weighted Correlation Coefficients Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Vidnerová, Petra (UIVT-O) RID, SAI, ORCIDSource Title Artificial 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 Pages s. 200-212 Number of pages 13 s. Publication form Print - P Action ICANN 2023: International Conference on Artificial Neural Networks /32./ Event date 26.09.2023 - 29.09.2023 VEvent location Heraklion Country GR - Greece Event type WRD Language eng - English Country CH - Switzerland Keywords Correlation coefficient ; Outliers ; Robustness ; Image analysis ; Approximate computing OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA22-02067S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 001157308600017 EID SCOPUS 85174632407 DOI 10.1007/978-3-031-44201-8_17 Annotation Pearson 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2024 Electronic address https://dx.doi.org/10.1007/978-3-031-44201-8_17
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