Visualization of Labeled Mixed-featured Datasets
This addresses visualization challenges for researchers analyzing complex mixed-feature datasets, though it appears incremental as it builds on existing projection and visualization techniques.
The paper tackles the problem of visualizing labeled datasets with mixed continuous and discrete features by developing a methodology that uses Max-Ratio Projection for dimensionality reduction and Radviz3D for display, with an available R package implementation.
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.