Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VIS is needed. In this paper, we systematically survey \paperNum ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?" This survey reveals six main processes where the employment of ML techniques can benefit visualizations: VIS-driven Data Processing, Data Presentation, Insight Communication, Style Imitation, VIS Interaction, VIS Perception. The six processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations. Meanwhile, the six processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.io.

翻译：在机器学习(ML)的伟大成功激励下,研究人员将ML技术应用于可视化,以更好地设计、发展和评估可视化问题。这个称为ML4VIS的研究分支近年来正在日益引起研究关注。为了成功地将ML技术用于可视化,需要对ML4VIS的整合有条不紊的理解。在本文中,我们系统地调查了ML4VIS的模拟研究,目的是回答两个激励性的问题:“ML能够帮助什么视觉化进程?”和“ML技术如何用来解决可视化问题?”这一调查揭示了六个主要过程,使用ML技术可以使可视化受益:VI数据处理、数据演示、透视通信、Styimmimation、VIS互动、VIS Pervition。这六个过程与ML4S管道的现有可视化理论模型有关,目的是说明ML协助ML4的可视化作用。同时,ML4的ML4网络化的六个过程被引入了ML的学习任务,ML4的当前视觉领域需要的MS的视觉能力。ML领域,这是ML4的今后需要的视觉领域。