ExplorerTree: A Focus+Context Exploration Approach for 2D Embeddings

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorDalhousie University-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorMarcílio-Jr, Wilson E. [UNESP]-
Autor(es): dc.creatorEler, Danilo M. [UNESP]-
Autor(es): dc.creatorPaulovich, Fernando V.-
Autor(es): dc.creatorRodrigues-Jr, José F.-
Autor(es): dc.creatorArtero, Almir O. [UNESP]-
Data de aceite: dc.date.accessioned2022-08-04T22:09:46Z-
Data de disponibilização: dc.date.available2022-08-04T22:09:46Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2021-07-15-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.bdr.2021.100239-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/221779-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/221779-
Descrição: dc.descriptionIn exploratory tasks involving high-dimensional datasets, dimensionality reduction (DR) techniques help analysts to discover patterns and other useful information. Although scatter plot representations of DR results allow for cluster identification and similarity analysis, such a visual metaphor presents problems when the number of instances of the dataset increases, resulting in cluttered visualizations. In this work, we propose a scatter plot-based multilevel approach to display DR results and address clutter-related problems when visualizing large datasets, together with the definition of a methodology to use focus+context interaction on non-hierarchical embeddings. The proposed technique, called ExplorerTree, uses a sampling selection technique on scatter plots to reduce visual clutter and guide users through exploratory tasks. We demonstrate ExplorerTree's effectiveness through a use case, where we visually explore activation images of the convolutional layers of a neural network. Finally, we also conducted a user experiment to evaluate ExplorerTree's ability to convey embedding structures using different sampling strategies.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionFaculty of Sciences and Technology São Paulo State University (UNESP)-
Descrição: dc.descriptionFaculty of Computer Science Dalhousie University-
Descrição: dc.descriptionInstitute of Mathematics and Computer Sciences University of São Paulo-
Descrição: dc.descriptionFaculty of Sciences and Technology São Paulo State University (UNESP)-
Descrição: dc.descriptionFAPESP: 2016/11707-6-
Descrição: dc.descriptionFAPESP: 2017/17450-0-
Descrição: dc.descriptionFAPESP: 2018/17881-3-
Descrição: dc.descriptionFAPESP: 2018/25755-8-
Idioma: dc.languageen-
Relação: dc.relationBig Data Research-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDimensionality reduction-
Palavras-chave: dc.subjectFocus+context-
Palavras-chave: dc.subjectScatter-plot-
Palavras-chave: dc.subjectVisualization-
Título: dc.titleExplorerTree: A Focus+Context Exploration Approach for 2D Embeddings-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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