Sampling techniques are widely used in the effort to reduce complexity and improve interpretability of datasets. Given the enormous availability of data, these techniques try to select representative data points that inherently reflect the data structure. In this work, we propose a novel sampling technique that preserves the structures imposed by dimensionality reduction techniques when visualized as scatter plots. In the experiments, we demonstrate how our technique is able to reflect the class boundaries and layout structures, besides decreasing redundancy of the datasets visualized as scatter plots. We also provide an user experiment regarding the perception of sampling from scatter plot visualizations.

中文翻译：

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SADIRE：一种用于降维可视化的上下文保留采样技术

采样技术被广泛用于降低复杂性和提高数据集的可解释性。鉴于数据的巨大可用性，这些技术试图选择固有地反映数据结构的代表性数据点。在这项工作中，我们提出了一种新颖的采样技术，当可视化为散点图时，该技术保留了降维技术强加的结构。在实验中，我们展示了我们的技术如何能够反映类边界和布局结构，以及减少可视化为散点图的数据集的冗余。我们还提供了一个关于从散点图可视化中采样感知的用户实验。