Cluster and then Embed: A Modular Approach for Visualization

• URL: http://arxiv.org/abs/2509.03373v1
• Date: Wed, 27 Aug 2025 00:27:30 GMT
• Title: Cluster and then Embed: A Modular Approach for Visualization
• Authors: Elizabeth Coda, Ery Arias-Castro, Gal Mishne,
• Abstract summary: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure.<n>We propose a more transparent, modular approach consisting of first clustering the data, then embedding each cluster, and finally aligning the clusters to obtain a global embedding.
• Score: 10.3849658049128
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure. They are known to group data points at the same time as they embed them, resulting in visualizations with well-separated clusters that preserve local information well. However, t-SNE and UMAP also tend to distort the global geometry of the underlying data. We propose a more transparent, modular approach consisting of first clustering the data, then embedding each cluster, and finally aligning the clusters to obtain a global embedding. We demonstrate this approach on several synthetic and real-world datasets and show that it is competitive with existing methods, while being much more transparent.

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