Properties of the Weighted and Robust Implicitly Weighted Correlation Coefficients

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00577080" target="_blank" >RIV/67985807:_____/23:00577080 - isvavai.cz</a>

Výsledek na webu

<a href="https://dx.doi.org/10.1007/978-3-031-44201-8_17" target="_blank" >https://dx.doi.org/10.1007/978-3-031-44201-8_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-44201-8_17" target="_blank" >10.1007/978-3-031-44201-8_17</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Properties of the Weighted and Robust Implicitly Weighted Correlation Coefficients

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Properties of the Weighted and Robust Implicitly Weighted Correlation Coefficients

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Aproximativní neurovýpočty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Artificial Neural Networks and Machine Learning – ICANN 2023. Proceedings, Part IX

ISBN

978-3-031-44200-1

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

13

Strana od-do

200-212

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Heraklion

Datum konání akce

26. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001157308600017