Optimal Sets of Projections of High-Dimensional Data
Optimal Sets of Projections of High-Dimensional Data
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DOI：
10.1109/tvcg.2015.2467132
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发表时间：
2016-01
影响因子：
5.2
通讯作者：
D. Lehmann;H. Theisel
中科院分区：
文献类型：
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作者：
D. Lehmann;H. Theisel
如何将n维数据集投影到二维可视化域是信息可视化中的重要问题之一。用户感兴趣的是通过探索最少数量的投影来获得对数据的最大洞察力。但是，如果数量太小或使用了不适当的预测，则可能会忽略重要的数据模式。我们提出了一种数据驱动的方法来找到最小的投影集，唯一地显示某些数据模式。为此，我们引入了一个相异的数据投影，丢弃仿射变换的投影，并防止重复相同的数据模式的措施。在此基础上，我们提供了最多n/2个投影的完整数据图尔斯。此外，我们提出了最佳路径的投影矩阵的交互式数据探索。我们用一组最先进的真实的高维基准数据集来说明我们的技术。
Finding good projections of n-dimensional datasets into a 2D visualization domain is one of the most important problems in Information Visualization. Users are interested in getting maximal insight into the data by exploring a minimal number of projections. However, if the number is too small or improper projections are used, then important data patterns might be overlooked. We propose a data-driven approach to find minimal sets of projections that uniquely show certain data patterns. For this we introduce a dissimilarity measure of data projections that discards affine transformations of projections and prevents repetitions of the same data patterns. Based on this, we provide complete data tours of at most n/2 projections. Furthermore, we propose optimal paths of projection matrices for an interactive data exploration. We illustrate our technique with a set of state-of-the-art real high-dimensional benchmark datasets.