Progressive dimensionality reduction algorithms allow for visually investigating intermediate results, especially for large data sets. While different algorithms exist that progressively increase the number of data points, we propose an algorithm that allows for increasing the number of dimensions. Especially in spatio-temporal data, where each spatial location can be seen as one data point and each time step as one dimension, the data is often stored in a format that supports quick access to the individual dimensions of all points. Therefore, we propose Progressive Glimmer, a progressive multidimensional scaling (MDS) algorithm. We adapt the Glimmer algorithm to support progressive updates for changes in the data's dimensionality. We evaluate Progressive Glimmer's embedding quality and runtime. We observe that the algorithm provides more stable results, leading to visually consistent results for progressive rendering and making the approach applicable to streaming data. We show the applicability of our approach to spatio-temporal simulation ensemble data where we add the individual ensemble members progressively.

翻译：渐进式降维算法允许对中间结果进行可视化研究，尤其适用于大规模数据集。尽管存在多种逐步增加数据点数量的算法，我们提出了一种能够增加维度数量的算法。特别是在时空数据中，每个空间位置可视为一个数据点，每个时间步可视为一个维度，此类数据通常以支持快速访问所有点各维度的格式存储。为此，我们提出渐进式微光算法——一种渐进式多维缩放算法。我们改进了微光算法以支持数据维度变化的渐进式更新。我们评估了渐进式微光算法的嵌入质量与运行效率，发现该算法能提供更稳定的结果，为渐进式渲染带来视觉一致性的输出，并使该方法适用于流式数据。我们通过逐步添加时空模拟集合数据中各个集合成员的方式，展示了本方法的实际适用性。