Tessellation-based Kernel Density Estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354356" target="_blank" >RIV/68407700:21230/21:00354356 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/21:00354356
Result on the web
<a href="https://doi.org/10.1145/3508546.3508582" target="_blank" >https://doi.org/10.1145/3508546.3508582</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3508546.3508582" target="_blank" >10.1145/3508546.3508582</a>

Result language
angličtina
Original language name
Tessellation-based Kernel Density Estimation
Original language description
Kernel density estimation is a complex task that plays an essential role in a variety of applications. In this paper, we introduce an approach to the task that converts the problem of bandwidth evaluation in the Parzen-window-like framework into the non-parametric evaluation of a fine-grained density estimate which can then be scaled by means of the Scale-Space theory to achieve the desired level of smoothness. The detailed estimate is realized through the Delaunay space tessellation method and properties of its output simplices. Additionally, in the experimental part of the paper, we showcase the new method and demonstrate its outputs at various scales, reaching results that perceivably outperform its counterparts.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
ACAI 2021: Proceedings of the 2021 International Conference on Algorithms, Computing and Artificial Intelligence
ISBN
978-1-4503-8505-3
ISSN
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e-ISSN
—
Number of pages
6
Pages from-to
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Publisher name
Association for Computing Machinery
Place of publication
New York
Event location
Sanya
Event date
Dec 22, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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