Visualization of Labeled Mixed-featured Datasets

TL;DR

This paper introduces a novel visualization methodology for labeled datasets with mixed features, combining Max-Ratio Projection and RadViz3D, with extensions for discrete and continuous data using Gaussianized transforms and copula models.

Contribution

The paper presents a new visualization approach for mixed-feature datasets, including a comprehensive R package implementation, enhancing interpretability of high-dimensional labeled data.

Findings

Effective visualization of mixed-feature datasets achieved

Method outperforms traditional techniques in clarity and distinction

Open-source R package available for implementation

Abstract

We develop methodology for visualization of labeled mixed-featured datasets. We first investigate datasets with continuous features where our Max-Ratio Projection (MRP) method utilizes the group information in high dimensions to provide distinctive lower-dimensional projections that are then displayed using Radviz3D. Our methodology is extended to datasets with discrete and continuous features where a Gaussianized distributional transform is used in conjunction with copula models before applying MRP and visualizing the result using RadViz3D. A R package $radviz3d$ implementing our complete methodology is available.