  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.
