High-Dimensional Data Classification in Concentric Coordinates

TL;DR

This paper introduces a novel visualization framework using lossless Concentric Coordinates for high-dimensional data, enabling better interpretability, reduced occlusion, and support for machine learning visualization and interaction.

Contribution

It proposes a new low-to-high dimensional visualization method using Concentric Coordinates, extending Parallel and Circular Coordinates for improved interpretability and computational efficiency.

Findings

Supports direct visualization of machine learning algorithms

Reduces occlusion in high-dimensional data visualization

Facilitates human interaction with complex data

Abstract

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.