Data visualization is the process by which data of any size or dimensionality is processed to produce an understandable set of data in a lower dimensionality, allowing it to be manipulated and understood more easily by people. The goal of our paper is to survey the performance of current high-dimensional data visualization techniques and quantify their strengths and weaknesses through relevant quantitative measures, including runtime, memory usage, clustering quality, separation quality, global structure preservation, and local structure preservation. To perform the analysis, we select a subset of state-of-the-art methods. Our work shows how the selected algorithms produce embeddings with unique qualities that lend themselves towards certain tasks, and how each of these algorithms are constrained by compute resources.

中文翻译：

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高维数据可视化近期趋势分析调查

数据可视化是处理任何大小或维度的数据以生成一组可理解的低维度数据的过程，使其更容易被人们操纵和理解。我们论文的目标是调查当前高维数据可视化技术的性能，并通过相关的量化指标量化它们的优缺点，包括运行时间、内存使用、聚类质量、分离质量、全局结构保存和局部结构保存。为了进行分析，我们选择了最先进方法的一个子集。我们的工作展示了所选算法如何生成具有独特品质的嵌入，这些嵌入适合某些任务，以及这些算法中的每一个如何受到计算资源的约束。