Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective
This addresses reliability issues in visualizing high-dimensional data for biomedical and deep learning applications, offering incremental improvements to existing methods.
The paper tackled the problem of misleading visual artifacts in neighbor embedding methods like t-SNE and UMAP by introducing LOO-map, a framework that extends embedding maps to the entire input space, identifying two forms of map discontinuity that distort visualizations and developing diagnostic scores to detect unreliable points and improve hyperparameter selection, validated on computer vision and single-cell omics datasets.
Visualizing high-dimensional data is essential for understanding biomedical data and deep learning models. Neighbor embedding methods, such as t-SNE and UMAP, are widely used but can introduce misleading visual artifacts. We find that the manifold learning interpretations from many prior works are inaccurate and that the misuse stems from a lack of data-independent notions of embedding maps, which project high-dimensional data into a lower-dimensional space. Leveraging the leave-one-out principle, we introduce LOO-map, a framework that extends embedding maps beyond discrete points to the entire input space. We identify two forms of map discontinuity that distort visualizations: one exaggerates cluster separation and the other creates spurious local structures. As a remedy, we develop two types of point-wise diagnostic scores to detect unreliable embedding points and improve hyperparameter selection, which are validated on datasets from computer vision and single-cell omics.