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

Xiaotong Liu, Zeyu Zhang, Hong Xuan, Roxana Leontie, Abby Stylianou, Robert Pless; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 2550-2558

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 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.

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[bibtex]
@InProceedings{Liu_2020_WACV,
author = {Liu, Xiaotong and Zhang, Zeyu and Xuan, Hong and Leontie, Roxana and Stylianou, Abby and Pless, Robert},
title = {2-MAP: Aligned Visualizations for Comparison of High-Dimensional Point Sets},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}