Among the challenges of the big data era, the analysis of high-dimensional data is still an open research area. As a result, several multidimensional projection techniques have been developed to reduce data dimensionality, becoming important visualization and visual analytics tools. In order to ensure the quality of projections, it is necessary to assess the low-dimensional embeddings by using different dataset configurations as input and analyzing evaluation metrics. However, it is not clear to the user how factors such as the number of dimensions, instances, or clusters, can affect the projection mapping and its quality regarding different projection techniques and assessment metrics. The research reported in this paper aims to clarify how much these factors affect each response variable via performance evaluation planning. We present an evaluation approach, supported by factorial design, that carries out a complete analysis, in the sense of measuring all possible combinations of all the input factors. The results of the analyses of local and global structure preservation in the projections yield a better understanding of how distinct dataset properties can influence the choice of projections based on quality metrics results.

在大数据时代的挑战中，对高维数据的分析仍然是一个开放的研究领域。结果，已经开发了几种多维投影技术来减小数据维数，成为重要的可视化和视觉分析工具。为了确保投影的质量，有必要通过使用不同的数据集配置作为输入并分析评估指标来评估低维嵌入。但是，对于用户而言，尚不清楚诸如维度，实例或群集的数量之类的因素如何影响投影映射及其关于不同投影技术和评估指标的质量。本文报道的研究旨在通过绩效评估计划来阐明这些因素在多大程度上影响每个响应变量。从度量所有输入因子的所有可能组合的意义上讲，我们提出了一种在因子设计的支持下进行评估的评估方法。投影中局部和全局结构保存的分析结果更好地了解了基于质量度量结果的不同数据集属性如何影响投影的选择。