UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data

TL;DR

This paper introduces NNInv, a deep learning method that approximates inverse projections of high-dimensional data visualizations, enabling interactive exploration and analysis of complex data structures.

Contribution

The paper presents NNInv, a novel deep learning approach for inverse projection in visualization, allowing reconstruction of high-dimensional data from 2D projections.

Findings

NNInv effectively reconstructs high-dimensional data from 2D projections.

The method supports interactive tasks like instance interpolation and classifier analysis.

Validation shows NNInv's accuracy and utility across multiple visualization scenarios.

Abstract

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection -- the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this paper we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.