Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Ohi, Abu Quwsar | |
dc.contributor.author | Gavrilova, Marina | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2024-07-29T18:28:01Z | - |
dc.date.available | 2024-07-29T18:28:01Z | - |
dc.date.issued | 2024 | |
dc.identifier.citation | WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 255-262. | en |
dc.identifier.issn | 2464–4625 (online) | |
dc.identifier.issn | 2464–4617 (print) | |
dc.identifier.uri | http://hdl.handle.net/11025/57397 | |
dc.description.sponsorship | The authors acknowledge the Natural Sciences and Engineering Research Council (NSERC) Discovery Grant funding, as well as the NSERC Strategic Partnership Grant (SPG) and the University of Calgary Transdisciplinary Connector Funding for the partial funding of this project. | cs_CZ |
dc.format | 8 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | en |
dc.rights | © Václav Skala - UNION Agency | en |
dc.subject | Laguerrova geometrie | cs |
dc.subject | Voronoiův diagram | cs |
dc.subject | shlukování | cs |
dc.subject | KMeans | cs |
dc.subject | klesající gradient | cs |
dc.title | LVCluster: Bounded Clustering using Laguerre Voronoi Diagram | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | Clustering, a fundamental technique in unsupervised learning, identifies similar groups within a dataset. However, clustering algorithms encounter limitations when requiring a predetermined number of clusters/centroids/labels. This paper proposes a novel approach of clustering by integrating concepts from Voronoi diagrams in Laguerre geometry, namely, Laguerre Voronoi Clustering (LVCluster). Laguerre geometry introduces circles by adding radius weight metric to centroids, enabling dynamic exclusion from clustering criteria. Consequently, this approach offers flexibility by necessitating only one hyperparameter, an upper-bound value for the number of circles. LVCluster can be optimized using gradient descent and can be jointly optimized with deep neural network architectures. The experimental results indicated that LVCluster outperforms clustering algorithms when trained individually and jointly with deep neural networks on increased cluster centroids. | en |
dc.subject.translated | Laguerre Geometry | en |
dc.subject.translated | Voronoi Diagram | en |
dc.subject.translated | clustering | en |
dc.subject.translated | KMeans | en |
dc.subject.translated | gradient descending | en |
dc.identifier.doi | https://doi.org/10.24132/10.24132/CSRN.3401.26 | |
dc.type.status | Peer reviewed | en |
Vyskytuje se v kolekcích: | WSCG 2024: Full Papers Proceedings |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
C53-2024.pdf | Plný text | 1,21 MB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/57397
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