In this paper,we develop a local-to-global and measure-theoretical approach to understand datasets. The idea is to take network models with restricted domains as local charts of datasets. We develop the mathematical foundations for these structures, and show in experiments how it can be used to find fuzzy domains and to improve accuracy in data classification problems.

翻译：本文提出了一种从局部到全局、基于测度论的方法来理解数据集。其基本思想是将具有受限域的网络模型视为数据集的局部坐标图。我们建立了这些结构的数学基础，并通过实验展示了如何利用该方法寻找模糊域以及提高数据分类问题的准确性。