论文标题
嵌入功能数据：多维缩放和流动学习
Embedding Functional Data: Multidimensional Scaling and Manifold Learning
论文作者
论文摘要
我们将最初在多维扩展和降低维度降低的领域中发展为功能设置。我们专注于经典缩放和ISOMAP - 在这些领域中起重要作用的原型方法 - 并在功能数据分析的背景下展示它们的使用。在此过程中，我们强调了环境公制扮演的关键作用。
We adapt concepts, methodology, and theory originally developed in the areas of multidimensional scaling and dimensionality reduction for multivariate data to the functional setting. We focus on classical scaling and Isomap -- prototypical methods that have played important roles in these area -- and showcase their use in the context of functional data analysis. In the process, we highlight the crucial role that the ambient metric plays.
