Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing multidimensional data. However, when data is a live streaming feed, conventional DR methods cannot be directly used because of their computational complexity and inability to preserve the projected data positions at previous time points. In addition, the problem becomes even more challenging when the dynamic data records have a varying number of dimensions as often found in real-world applications. This paper presents an incremental DR solution. We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data. First, we use geometric transformation and animation methods to help preserve a viewer's mental map when visualizing the incremental results. Second, to handle data dimension variants, we use an optimization method to estimate the projected data positions, and also convey the resulting uncertainty in the visualization. We demonstrate the effectiveness of our design with two case studies using real-world datasets.

中文翻译：

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用于可视化流多维数据的增量降维方法。

降维（DR）方法通常用于分析和可视化多维数据。但是，当数据是实时流式提要时，常规的DR方法由于其计算复杂性和无法保留先前时间点的投影数据位置而无法直接使用。此外，当动态数据记录的维数变化不定时（如在实际应用中经常发现的那样），该问题将变得更加具有挑战性。本文提出了一种增量灾难恢复解决方案。我们以几种方式增强了现有的增量PCA方法，以确保其可用于可视化多维数据流。首先，当可视化增量结果时，我们使用几何变换和动画方法来帮助保留观看者的心理图。其次，要处理数据维度变体，我们使用一种优化方法来估计预计的数据位置，并在可视化中传达由此产生的不确定性。我们通过使用真实数据集的两个案例研究证明了我们设计的有效性。