Nonato Luis Gustavo, Aupetit Michael
IEEE Trans Vis Comput Graph. 2019 Aug;25(8):2650-2673. doi: 10.1109/TVCG.2018.2846735. Epub 2018 Jun 13.
Visual analysis of multidimensional data requires expressive and effective ways to reduce data dimensionality to encode them visually. Multidimensional projections (MDP) figure among the most important visualization techniques in this context, transforming multidimensional data into scatter plots whose visual patterns reflect some notion of similarity in the original data. However, MDP come with distortions that make these visual patterns not trustworthy, hindering users to infer actual data characteristics. Moreover, the patterns present in the scatter plots might not be enough to allow a clear understanding of multidimensional data, motivating the development of layout enrichment methodologies to operate together with MDP. This survey attempts to cover the main aspects of MDP as a visualization and visual analytic tool. It provides detailed analysis and taxonomies as to the organization of MDP techniques according to their main properties and traits, discussing the impact of such properties for visual perception and other human factors. The survey also approaches the different types of distortions that can result from MDP mappings and it overviews existing mechanisms to quantitatively evaluate such distortions. A qualitative analysis of the impact of distortions on the different analytic tasks performed by users when exploring multidimensional data through MDP is also presented. Guidelines for choosing the best MDP for an intended task are also provided as a result of this analysis. Finally, layout enrichment schemes to debunk MDP distortions and/or reveal relevant information not directly inferable from the scatter plot are reviewed and discussed in the light of new taxonomies. We conclude the survey providing future research axes to fill discovered gaps in this domain.
对多维数据进行可视化分析需要有表现力且有效的方法来降低数据维度，以便进行可视化编码。在这种情况下，多维投影（MDP）是最重要的可视化技术之一，它将多维数据转换为散点图，其视觉模式反映了原始数据中的某种相似性概念。然而，MDP 会带来失真，使得这些视觉模式不可靠，阻碍用户推断实际数据特征。此外，散点图中呈现的模式可能不足以让人清晰地理解多维数据，这促使了布局增强方法的发展，以便与 MDP 一起使用。本综述试图涵盖 MDP 作为一种可视化和视觉分析工具的主要方面。它根据 MDP 技术的主要属性和特征，对其组织方式进行了详细分析和分类，讨论了这些属性对视觉感知和其他人为因素的影响。该综述还探讨了 MDP 映射可能导致的不同类型的失真，并概述了用于定量评估此类失真的现有机制。还对通过 MDP 探索多维数据时失真对用户执行的不同分析任务的影响进行了定性分析。作为该分析的结果，还提供了针对特定任务选择最佳 MDP 的指南。最后，根据新的分类法，对用于消除 MDP 失真和/或揭示散点图中无法直接推断的相关信息的布局增强方案进行了综述和讨论。我们在综述结尾提出了未来的研究方向，以填补该领域发现的空白。
