# High-Dimensional Data Classification in Concentric Coordinates

arXiv: https://arxiv.org/abs/2507.18450 · HTML: https://mlsanity.com/paper/2507.18450/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 7 of 7)

- [TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization](https://mlsanity.com/paper/2209.09227/) (2022)
- [Learning How to Vote with Principles: Axiomatic Insights Into the Collective Decisions of Neural Networks](https://mlsanity.com/paper/2410.16170/) (2024)
- [Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates](https://mlsanity.com/paper/2107.04974/) (2021)
- [Visual Knowledge Discovery with General Line Coordinates](https://mlsanity.com/paper/2305.18429/) (2023)
- [Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling](https://mlsanity.com/paper/2305.18432/) (2023)
- [Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates](https://mlsanity.com/paper/2409.02079/) (2024)
- [Boosting of Classification Models with Human-in-the-Loop Computational Visual Knowledge Discovery](https://mlsanity.com/paper/2502.07039/) (2025)

## Abstract (arXiv)

The visualization of multi-dimensional data with interpretable methods remains limited by capabilities for both high-dimensional lossless visualizations that do not suffer from occlusion and that are computationally capable by parameterized visualization. This paper proposes a low to high dimensional data supporting framework using lossless Concentric Coordinates that are a more compact generalization of Parallel Coordinates along with former Circular Coordinates. These are forms of the General Line Coordinate visualizations that can directly support machine learning algorithm visualization and facilitate human interaction.