2-MAP: Aligned Visualizations for Comparison of High-Dimensional Point Sets

Xiaotong Liu, Zeyu Zhang, Roxana Leontie, Abby Stylianou, Robert Pless

Abstract: Visualization tools like t-SNE and UMAP give insight into the high-dimensional structure of datasets. When there are related datasets (such as the high-dimensional representations of image data created by two different Deep Learning architectures), roughly aligning those visualizations helps to highlight both the similarities and differences. In this paper we propose a method to align multiple low dimensional UMAP visualizations by adding an alignment term to the UMAP loss function. We provide an automated procedure to find a weight for this term that encourages the alignment but only minimally changes the fidelity of the underlying embedding.