Properties of the Weighted and Robust Implicitly Weighted Correlation Coefficients
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Properties of the Weighted and Robust Implicitly Weighted Correlation Coefficients
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks and Machine Learning – ICANN 2023. Proceedings, Part IX
ISBN
978-3-031-44200-1
ISSN
0302-9743
e-ISSN
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Number of pages
13
Pages from-to
200-212
Publisher name
Springer
Place of publication
Cham
Event location
Heraklion
Event date
Sep 26, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001157308600017