MEDALLION: Manifold Embedding Distillation and Attributions for Low-dimensional Latent InterpretatiON

Vicky Jung; Claire M. He; Irene Chang; Genevera I. Allen

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arXiv · 2611.08767

MEDALLION: Manifold Embedding Distillation and Attributions for Low-dimensional Latent InterpretatiON

Vicky Jung·Claire M. He·Irene Chang·Genevera I. Allen

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Abstract

Dimensionality reduction is widely used to visualize high-dimensional data and guide scientific discovery, yet interpreting which original features contribute to low-dimensional representations remains challenging, particularly for black-box methods such as t-SNE and UMAP. We introduce \textbf{MEDALLION} (Manifold Embedding Distillation and Attributions for Low-dimensional Latent InterpretatiON), a novel framework for interpreting low-dimensional embeddings through knowledge distillation and neural feature attribution. MEDALLION distills any given embedding with a neural encoder which we explain via a broad range of existing neural feature attribution methods. Through experiments on MNIST and applications in single-cell transcriptomics, we show MEDALLION provides informative feature attributions that cast additional insights into the underlying embedding and compare its performance with existing interpretability approaches.

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Vicky Jung, Claire M. He, Irene Chang, Genevera I. Allen. 2026-11-05. MEDALLION: Manifold Embedding Distillation and Attributions for Low-dimensional Latent InterpretatiON. https://arxiv.org/abs/2611.08767

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