How to proper initialize Gaussian Mixture Models with Optimum-Path Forest

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorMartins, Guilherme Brandao-
Autor(es): dc.creatorPapa, Joao Paulo-
Data de aceite: dc.date.accessioned2025-08-21T17:31:00Z-
Data de disponibilização: dc.date.available2025-08-21T17:31:00Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991796-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248222-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248222-
Descrição: dc.descriptionIn this paper, we proposed a fast and scalable unsupervised Optimum-Path Forest for improving the initialization of Gaussian mixture models. Taking advantage of Optimum-Path Forest attributes such as on-the-fly number of clusters estimation and its intrinsic non-parametric nature, we exploited the k Approximate Nearest Neighbors graph to build its adjacency relation, enabling it not only to initialize the Expectation-Maximization algorithm but to be employed for clustering on large datasets as well. From experiments conducted on eight datasets, the results indicated the proposed approach is able to encode Gaussian parameters more naturally and intuitively compared to other clustering algorithms such as $k -$means. Furthermore, the proposed approach has shown great scalability, making it a viable alternative to traditional Optimum-Path Forest clustering-
Descrição: dc.descriptionFederal University of São Carlos - UFSCar São Carlos Department of Computing, S˜ao Carlos-
Descrição: dc.descriptionSão Paulo State University - Unesp Bauru Department of Computing, Bauru-
Descrição: dc.descriptionSão Paulo State University - Unesp Bauru Department of Computing, Bauru-
Formato: dc.format127-132-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2022 35th Conference on Graphics, Patterns, and Images, SIBGRAPI 2022-
???dc.source???: dc.sourceScopus-
Título: dc.titleHow to proper initialize Gaussian Mixture Models with Optimum-Path Forest-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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