DR-SNE: Density-Regularized Stochastic Neighbor Embedding
Dimensionality-reduction methods such as t-SNE preserve local neighborhood structure but can substantially distort the local distribution of data. We introduce Density-Regularized Stochastic Neighbor Embedding (DR-SNE), which augments stochastic neighbor embedding with a regularizer that directly aligns normalized inverse-neighborhood-scale profiles between the original and embedding spaces. The resulting objective preserves relative local concentration while remaining invariant to global rescaling of the embedding. Across real and synthetic datasets, DR-SNE generally improves preservation of the targeted concentration profile while maintaining controlled levels of neighborhood fidelity. ...
| Authors | Maksim Kazanskii |
|---|---|
| Primary category | cs.LG |
| Published | 3 May 2026 |
| Last updated at source | 24 August 2026 |
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