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

• Autor(a) principal: Marcílio-Jr, Wilson E. [UNESP]
• Data de Publicação: 2021
• Outros Autores: Eler, Danilo M. [UNESP], Paulovich, Fernando V., Rodrigues-Jr, José F., Artero, Almir O. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.bdr.2021.100239; http://hdl.handle.net/11449/221779
• topic: Dimensionality reduction; Focus+context; Scatter-plot; Visualization

In 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.