论文标题
对矢量字段的数据建模的简短审查
A Short Review on Data Modelling for Vector Fields
论文作者
论文摘要
基于统计原理的机器学习方法已被证明在处理各种数据分析和分析任务方面已经非常成功。传统数据模型主要与独立分布的数据有关。端到端建模方案的最新成功使用配备有效结构（例如卷积层或跳过连接）的深神经网络的成功允许扩展到更复杂和结构化的实践数据，例如自然语言，图像，视频等。在应用方面，矢量领域是经验科学的数据类型非常有用的类型，以及信号处理，以及信号处理。 3D点云的非参数转换使用3D矢量场，地球科学中流体流的建模以及物理场的建模。 这篇评论文章专用于矢量字段的最新计算工具，包括向量数据表示，空间数据的预测模型以及计算机视觉，信号处理和经验科学的应用。
Machine learning methods based on statistical principles have proven highly successful in dealing with a wide variety of data analysis and analytics tasks. Traditional data models are mostly concerned with independent identically distributed data. The recent success of end-to-end modelling scheme using deep neural networks equipped with effective structures such as convolutional layers or skip connections allows the extension to more sophisticated and structured practical data, such as natural language, images, videos, etc. On the application side, vector fields are an extremely useful type of data in empirical sciences, as well as signal processing, e.g. non-parametric transformations of 3D point clouds using 3D vector fields, the modelling of the fluid flow in earth science, and the modelling of physical fields. This review article is dedicated to recent computational tools of vector fields, including vector data representations, predictive model of spatial data, as well as applications in computer vision, signal processing, and empirical sciences.
