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arXiv Machine Learning By Henry Han, Luyu Niu, Diane Li, Jiacun Wang

Differential rank preservation manifold learning

Read the original on arXiv Machine Learning →

The paper introduces Smooth Rank t‑SNE (SR‑tSNE), a variant of t‑SNE that incorporates a differentiable rank term using a sigmoid approximation to preserve the ordering of neighbors in the embedding. It also proposes Ordinal DLP (O‑DLP), a metric that rewards neighbors only when they appear at their original rank. Across eight diverse benchmarks, SR‑tSNE outperforms 12 baselines in rank preservation, neighborhood overlap, and downstream tasks such as label transfer, cell‑type discovery, and intraday trading.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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