Visually enhanced exploration of high-dimensional data

我们建议创建新的软件技术来探索大型高维数据集，其中数据点的数量从数万到数百万不等。我们的工具将帮助人们洞察复杂的问题，这些问题取决于从几十到数百个变量之间的关系。以前处理这些问题的方法假设，只有在整个有限数据集可以加载到软件系统中之后，才会进行探索。我们还将解决不能简单地加载完整数据集的问题领域；必须首先进行采样，然后才能使用交互式可视化或机器学习来解释它。出现这种情况的原因有几个。有些数据集本质上是连续的，必须对其进行采样以对其进行离散化。其他数据集是有限的，但太大了，无法完整处理。在某些应用程序域中，采集样本的成本在计算上非常昂贵，因此应该只采集极少量的样本。

We propose to create new software technology for the exploration of large high-dimensional datasets, where the number of data points ranges from tens of thousands to millions. Our tools will help people develop insight into complex problems that depend on the relationships between many variables, ranging from tens to hundreds. Previous approaches to handling these problems assumed that exploration would only occur after an entire, finite dataset could be loaded into a software system. We will also address problem domains where the full dataset cannot simply be loaded; sampling must occur first, before interactive visualization or machine learning can be used to interpret it. This situation can occur for several reasons. Some datasets are inherently continuous and must be sampled to discretize them. Other datasets are finite but simply too large to handle in their entirety. In some application domains, the cost of taking a sample is so computationally expensive that only a minimal number of them should be taken.