A Data-Driven Approach for Mapping Multivariate Data to Color

TL;DR

This paper introduces a data-driven method for mapping high-dimensional multivariate data to color by assessing attribute similarity and using GBC interpolation within a convex 2D color space, overcoming previous scalability limitations.

Contribution

It proposes a novel approach that scales to high-dimensional data by leveraging attribute similarity and convex color space mapping, unlike existing methods.

Findings

Effective mapping of multivariate data to color demonstrated.

Scales to higher dimensions than traditional methods.

Uses GBC interpolation for smooth color transitions.

Abstract

A wide variety of color schemes have been devised for mapping scalar data to color. Some use the data value to index a color scale. Others assign colors to different, usually blended disjoint materials, to handle areas where materials overlap. A number of methods can map low-dimensional data to color, however, these methods do not scale to higher dimensional data. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. We address the challenge of mapping multivariate data to color and avoid these limitations at the same time. It is a data driven method, which first gauges the similarity of the attributes and then arranges them according to the periphery of a convex 2D color space, such as HSL. The color of a multivariate data sample is then obtained via generalized barycentric coordinate (GBC) interpolation.