# General Line Coordinates in 3D

arXiv: https://arxiv.org/abs/2403.13014 · HTML: https://mlsanity.com/paper/2403.13014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [Discovering Interpretable Machine Learning Models in Parallel Coordinates](https://mlsanity.com/paper/2106.07474/) (2021)
- [Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning](https://mlsanity.com/paper/2107.04971/) (2021)
- [Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models](https://mlsanity.com/paper/2205.04035/) (2022)
- [Visual Knowledge Discovery with General Line Coordinates](https://mlsanity.com/paper/2305.18429/) (2023)

## Abstract (arXiv)

Interpretable interactive visual pattern discovery in lossless 3D visualization is a promising way to advance machine learning. It enables end users who are not data scientists to take control of the model development process as a self-service. It is conducted in 3D General Line Coordinates (GLC) visualization space, which preserves all n-D information in 3D. This paper presents a system which combines three types of GLC: Shifted Paired Coordinates (SPC), Shifted Tripled Coordinates (STC), and General Line Coordinates-Linear (GLC-L) for interactive visual pattern discovery. A transition from 2-D visualization to 3-D visualization allows for a more distinct visual pattern than in 2-D and it also allows for finding the best data viewing positions, which are not available in 2-D. It enables in-depth visual analysis of various class-specific data subsets comprehensible for end users in the original interpretable attributes. Controlling model overgeneralization by end users is an additional benefit of this approach.