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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Pushing Visualization Research Frontiers: Essential Topics Not Addressed by Machine Learning.

Kwan-Liu Ma, Theresa-Marie Rhyne

IEEE Computer Graphics and Applications

|April 6, 2023

Summary

This viewpoint emphasizes the need to continue research in data visualization independent of machine learning (ML). Investing in ML-agnostic visualization is crucial for the field's advancement and exploring new frontiers.

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Area of Science:

Data Visualization
Human-Computer Interaction
Machine Learning

Background:

The field of data visualization is experiencing significant interest and investment in machine learning (ML) applications.
This trend risks overshadowing valuable research areas within visualization that are independent of ML.

Purpose of the Study:

To highlight the importance of ML-agnostic research in data visualization.
To identify and discuss future research challenges and opportunities not directly solvable by ML.

Main Methods:

This is a Viewpoints article, presenting a personal perspective.
Discussion of research challenges and opportunities in data visualization.

Main Results:

Identifies a critical need for continued investment in ML-agnostic visualization research.
Suggests that such research is vital for the overall growth and innovation within the data visualization field.

Conclusions:

The current focus on ML in visualization should not lead to the neglect of ML-agnostic approaches.
Continued exploration of ML-agnostic visualization is imperative for the field's progress and to unlock novel capabilities.