论文标题
铸造多个阴影：高维交互式数据可视化与游览和嵌入
Casting Multiple Shadows: High-Dimensional Interactive Data Visualisation with Tours and Embeddings
论文作者
论文摘要
非线性维度降低（NLDR）方法，例如T-分布的随机邻居嵌入（T-SNE）在自然科学中无处不在，但是，由于其复杂的参数化，因此很难使用这些方法。分析师必须进行权衡，以确定NLDR技术的可视化结构。我们通过将其与称为Tour的技术相结合，介绍了NLDR方法务实使用NLDR方法的视觉诊断。巡回演出是多元数据插值线性投影的序列。该序列显示为动态可视化，使用户可以在较低的维视图中看到阴影。通过将游览链接到NLDR视图，我们可以保留全局结构，并通过诸如链接刷子（链接刷子）观察NLDR视图可能会误导的位置。我们从模拟和单细胞转录组学中显示了几个案例研究，这些案例研究表明我们的方法对群集方向任务有用。
Non-linear dimensionality reduction (NLDR) methods such as t-distributed stochastic neighbour embedding (t-SNE) are ubiquitous in the natural sciences, however, the appropriate use of these methods is difficult because of their complex parameterisations; analysts must make trade-offs in order to identify structure in the visualisation of an NLDR technique. We present visual diagnostics for the pragmatic usage of NLDR methods by combining them with a technique called the tour. A tour is a sequence of interpolated linear projections of multivariate data onto a lower dimensional space. The sequence is displayed as a dynamic visualisation, allowing a user to see the shadows the high-dimensional data casts in a lower dimensional view. By linking the tour to an NLDR view, we can preserve global structure and through user interactions like linked brushing observe where the NLDR view may be misleading. We display several case studies from both simulations and single cell transcriptomics, that shows our approach is useful for cluster orientation tasks.
