VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation
<|reference_start|>VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation: Two-dimensional embeddings obtained from dimensionality reduction techniques, such as MDS, t-SNE, and UMAP, are widely used across various disciplines to visualize high-dimensional data. These visualizations provide a valuable tool for exploratory data analysis, allowing researchers to visually identify clusters, outliers, and other interesting patterns in the data. However, interpreting the resulting visualizations can be challenging, as it often requires additional manual inspection to understand the differences between data points in different regions of the embedding space. To address this issue, we propose Visual Explanations via Region Annotation (VERA), an automatic embedding-annotation approach that generates visual explanations for any two-dimensional embedding. VERA produces informative explanations that characterize distinct regions in the embedding space, allowing users to gain an overview of the embedding landscape at a glance. Unlike most existing approaches, which typically require some degree of manual user intervention, VERA produces static explanations, automatically identifying and selecting the most informative visual explanations to show to the user. We illustrate the usage of VERA on a real-world data set and validate the utility of our approach with a comparative user study. Our results demonstrate that the explanations generated by VERA are as useful as fully-fledged interactive tools on typical exploratory data analysis tasks but require significantly less time and effort from the user.<|reference_end|>
@article{poličar2024vera:,
    title={VERA: Generating Visual Explanations of Two-Dimensional Embeddings via
  Region Annotation},
    author={Pavlin G. Poliv{c}ar and Blav{z} Zupan},
    journal={arXiv preprint arXiv:2406.04808},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04808},
    primaryClass={cs.LG cs.HC}
}