Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2021
Abstract
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 article, we systematically survey 88 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 seven main processes where the employment of ML techniques can benefit visualizations: Data Processing4VIS, Data-VIS Mapping, Insight Communication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling . The seven 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 seven 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 article can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.io .
Keywords
ML4VIS, Machine Learning, Data Visualization, Survey
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Visualization and Computer Graphics
Volume
28
Issue
12
First Page
5134
Last Page
5153
ISSN
1077-2626
Identifier
10.1109/TVCG.2021.3106142
Publisher
Institute of Electrical and Electronics Engineers
Citation
WANG, Qianwen; CHEN, Zhutian; WANG, Yong; and QU, Huamin.
A survey on ML4VIS: Applying machine learning advances to data visualization. (2021). IEEE Transactions on Visualization and Computer Graphics. 28, (12), 5134-5153.
Available at: https://ink.library.smu.edu.sg/sis_research/7670
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TVCG.2021.3106142
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons