Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization
Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization
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DOI：
10.1109/tvcg.2014.2330617
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发表时间：
2015
影响因子：
5.2
通讯作者：
Ronak Etemadpour;Robson Motta;J. G. Paiva;R. Minghim;Maria Cristina Ferreira de Oliveira;L. Linsen
中科院分区：
文献类型：
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作者：
Ronak Etemadpour;Robson Motta;J. G. Paiva;R. Minghim;Maria Cristina Ferreira de Oliveira;L. Linsen
由多维投影或其他降维技术生成的基于相似性的布局通常用于可视化高维数据。最近提出了许多针对不同目标和应用领域的投影技术。然而，从用户的角度来看，对于生成的布局的有效性知之甚少，对于来自相同数据的不同布局如何比较它们所支持的典型可视化任务，或者特定领域的问题如何影响技术的结果。学习更多关于投影使用的知识是巩固它们在高维数据分析中的作用和在选择技术时做出明智决策的重要一步。这项工作为实现这一目标作出了贡献。我们描述了一项关于投影技术产生的布局性能的调查结果，这些布局由用户感知。我们进行了一项控制用户研究，以检验以下假设：(1)投影性能是任务依赖的；(2)某些预测在某些类型的任务上表现更好；(3)投影性能取决于数据的性质；(4)受试者偏好隔离能力较好的投影。我们使用代表不同投影方法的五种技术生成高维数据的布局。作为应用领域，我们研究了图像和文档数据。我们确定了8个典型任务，其中3个与投影的分离能力有关，3个与投影精度有关，2个与产生的视觉杂波有关。将问题答案的正确性与直接从数据中计算出来的“基本事实”进行比较。我们还研究了受试者的信心和完成任务的时间。对收集到的数据进行统计分析，假设1和3被证实，假设2被部分证实，假设4不能被证实。我们讨论了我们的研究结果，并与一些投影布局质量的数值测量进行了比较。我们的研究结果为在数据可视化任务中使用投影布局提供了有趣的见解，并为进一步的系统研究提供了一个出发点。
Similarity-based layouts generated by multidimensional projections or other dimension reduction techniques are commonly used to visualize high-dimensional data. Many projection techniques have been recently proposed addressing different objectives and application domains. Nonetheless, very little is known about the effectiveness of the generated layouts from a user's perspective, how distinct layouts from the same data compare regarding the typical visualization tasks they support, or how domain-specific issues affect the outcome of the techniques. Learning more about projection usage is an important step towards both consolidating their role in high-dimensional data analysis and taking informed decisions when choosing techniques. This work provides a contribution towards this goal. We describe the results of an investigation on the performance of layouts generated by projection techniques as perceived by their users. We conducted a controlled user study to test against the following hypotheses: (1) projection performance is task-dependent; (2) certain projections perform better on certain types of tasks; (3) projection performance depends on the nature of the data; and (4) subjects prefer projections with good segregation capability. We generated layouts of high-dimensional data with five techniques representative of different projection approaches. As application domains we investigated image and document data. We identified eight typical tasks, three of them related to segregation capability of the projection, three related to projection precision, and two related to incurred visual cluttering. Answers to questions were compared for correctness against `ground truth' computed directly from the data. We also looked at subject confidence and task completion times. Statistical analysis of the collected data resulted in Hypotheses 1 and 3 being confirmed, Hypothesis 2 being confirmed partially and Hypotheses 4 could not be confirmed. We discuss our findings in comparison with some numerical measures of projection layout quality. Our results offer interesting insight on the use of projection layouts in data visualization tasks and provide a departing point for further systematic investigations.