ShaRP: 形状正则化的多维投影
ShaRP: Shape-Regularized Multidimensional Projections
AI总结：
ShaRP是一种能够提供用户对投影形状显式控制的多维投影方法，适用于交互式可视化场景。
摘要：
投影，或降维方法，是用于高维数据可视化探索的技术首选。许多此类技术存在，每种方法都有独特的视觉特征——即一种可识别的方式在生成的散点图中排列点。这些特征是算法设计的隐含结果，例如方法是否侧重于局部或全局数据模式的保持；优化技术；以及超参数设置。我们提出了一种新的投影技术——ShaRP，它为用户提供对创建散点图的视觉特征的显式控制，这可以更好地适应交互式可视化场景。ShaRP在降维和数据集大小方面具有良好的扩展性，能够处理任何定量数据集，并以较小的用户可控的代价提供这种扩展功能，即控制投影形状。
英文摘要：
Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature - i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit consequences of algorithm design, such as whether the method focuses on local vs global data pattern preservation; optimization techniques; and hyperparameter settings. We present a novel projection technique - ShaRP - that provides users explicit control over the visual signature of the created scatterplot, which can cater better to interactive visualization scenarios. ShaRP scales well with dimensionality and dataset size, generically handles any quantitative dataset, and provides this extended functionality of controlling projection shapes at a small, user-controllable cost in terms of quality metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
