MEDAL: Manifold Embedding Distillation via Autoencoder Learning
Low-dimensional embeddings are widely used as visual summaries of high-dimensional data and to enable downstream scientific discoveries. Yet, popular nonlinear dimension reduction methods, such as t-SNE and UMAP, are often selected based on visual appeal alone and without rigorous quantitative validation. A major reason is that manifold embeddings typically do not provide an out-of-sample map nor an inverse back to the original feature space; this makes held-out validation, the gold standard in supervised learning, all but impossible. To address these challenges, we develop a novel framework, MEDAL (Manifold Embedding Distillation via Autoencoder Learning), which distills a fitted manifol...
| Authors | Irene Chang, Tarek M. Zikry, Genevera I. Allen |
|---|---|
| Primary category | stat.ML |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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