High-Dimensional Data Classification in Concentric Coordinates

Fuente: arXiv
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Main Authors: Williams, Alice, Kovalerchuk, Boris
Format: Preprint
Published: 2025
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author Williams, Alice
Kovalerchuk, Boris
author_facet Williams, Alice
Kovalerchuk, Boris
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2507_18450
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle High-Dimensional Data Classification in Concentric Coordinates
Williams, Alice
Kovalerchuk, Boris
Human-Computer Interaction
Machine Learning
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.
title High-Dimensional Data Classification in Concentric Coordinates
topic Human-Computer Interaction
Machine Learning
url https://arxiv.org/abs/2507.18450