General Line Coordinates in 3D
Joshua Martinez Boris Kovalerchuk
Interpretable interactive visual pattern discovery in lossless 3D
visualization is a promising way to advance machine learning. It enables end
users who are not data scientists to take control of the model development
process as a self-service. It is conducted in 3D General Line Coordinates (GLC)
visualization space, which preserves all n-D information in 3D. This paper
presents a system which combines three types of GLC: Shifted Paired Coordinates
(SPC), Shifted Tripled Coordinates (STC), and General Line Coordinates-Linear
(GLC-L) for interactive visual pattern discovery. A transition from 2-D
visualization to 3-D visualization allows for a more distinct visual pattern
than in 2-D and it also allows for finding the best data viewing positions,
which are not available in 2-D. It enables in-depth visual analysis of various
class-specific data subsets comprehensible for end users in the original
interpretable attributes. Controlling model overgeneralization by end users is
an additional benefit of this approach.