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.

在涉及高维数据集的探索性任务中，降维 (DR) 技术可帮助分析师发现模式和其他有用信息。尽管 DR 结果的散点图表示允许进行聚类识别和相似性分析，但当数据集的实例数量增加时，这种视觉隐喻会出现问题，导致可视化混乱。在这项工作中，我们提出了一种基于散点图的多级方法，用于在可视化大型数据集时显示 DR 结果并解决与杂波相关的问题，并定义了在非分层嵌入上使用焦点 + 上下文交互的方法。所提议的技术称为 ExplorerTree，它使用散点图上的抽样选择技术来减少视觉混乱并引导用户完成探索性任务。我们通过一个用例展示了 ExplorerTree 的有效性，在该用例中，我们直观地探索了神经网络卷积层的激活图像。最后，我们还进行了一项用户实验，以评估 ExplorerTree 使用不同采样策略传达嵌入结构的能力。