In high-dimensional data analysis the curse of dimensionality reasons that points tend to be far away from the center of the distribution and on the edge of high-dimensional space. Contrary to this, is that projected data tends to clump at the center. This gives a sense that any structure near the center of the projection is obscured, whether this is true or not. A transformation to reverse the curse, is defined in this paper, which uses radial transformations on the projected data. It is integrated seamlessly into the grand tour algorithm, and we have called it a burning sage tour, to indicate that it reverses the curse. The work is implemented into the tourr package in R. Several case studies are included that show how the sage visualizations enhance exploratory clustering and classification problems.
翻译:在高维数据分析中, 维度原因的诅咒往往远离分布的中心和高维空间边缘。 与此相反, 预测数据往往在中心倾斜。 这使人感到, 投影中心附近的任何结构都模糊不清, 不论这是否属实。 本文定义了扭转诅咒的转变, 它在预测数据上使用辐射转换。 它被无缝地融入了大巡回算法, 我们称之为“ 燃烧的精子巡演 ”, 以表明它反转诅咒。 这项工作在R. 的巡演包中实施。 包括了几个案例研究, 表明Sage可视化如何加强探索性集群和分类问题 。