论文标题
时间依赖性多维投影技术的定量评估
Quantitative Evaluation of Time-Dependent Multidimensional Projection Techniques
论文作者
论文摘要
降低降低方法是进行多维数据分析的必不可少的工具，并且可以将许多有趣的过程作为时间依赖于时间依赖的多元数据集进行研究。但是，在动态/时间数据的背景下，很少有研究和建议利用投影表达的简洁能力。在本文中，我们旨在提供一种评估动态数据投影技术的方法，并了解视觉质量和稳定性之间的关系。我们的方法依赖于实验设置，该设置由现有技术组成，专为时间依赖性数据和静态方法的新变化而设计。为了支持对这些技术的评估，我们提供了一个数据集的集合，这些数据集具有各种编码动态模式的特征，以及一组空间和时间稳定性指标来评估布局的质量。我们介绍了11种方法，10个数据集和12个质量指标的评估，并选择了预测时间依赖时间的多元数据的最合适的方法，从而探索了每种方法的设计选择和特征。我们的所有结果都记录在公共存储库中，以允许可重复性结果。
Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time-dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 11 methods, 10 datasets, and 12 quality metrics, and elect the best-suited methods for projecting time-dependent multivariate data, exploring the design choices and characteristics of each method. All our results are documented and made available in a public repository to allow reproducibility of results.
