先聚类再嵌入，提升可视化透明度与全局结构保持
Cluster and then Embed: A Modular Approach for Visualization
- 分三步：先聚类，再分块嵌入，最后对齐融合
- 在多个数据集上表现媲美t-SNE/UMAP，且全局结构更准确
- 适合需要可解释性的可视化场景，如科研分析
降维方法如t-SNE和UMAP常用于具有潜在聚类结构的数据可视化，它们在嵌入过程中同时进行聚类，生成局部信息保留良好、簇间分离清晰的图示。然而，这些方法往往扭曲数据的全局几何结构。本文提出一种更透明的模块化方法：先对数据进行聚类，再分别嵌入每个簇，最后对齐各簇以获得全局嵌入。我们在多个合成与真实数据集上验证该方法，结果表明其性能可与现有方法相媲美，同时具备更高的可解释性。
原文摘要 · Abstract (English)
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 that first clusters the data, then embeds each cluster, and finally aligns 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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