A general framework for adaptive nonparametric dimensionality reduction

arXiv:2511.09486 · stat.ML, cs.AI, cs.LG, stat.ME · Submitted 2025-11-12 · Read on arXiv

stat.ML, cs.AI, cs.LG, stat.ME

Submitted: 2025-11-12

Updated: 2026-01-26

Journal ref: Sci. Rep. 16 (2026) 9028

DOI: 10.1038/s41598-026-35847-1

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 17× in this paper · explained in 2 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Homogeneity — 11× in this paper · explained in 2 episodes
• Intrinsic Dimension — 9× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Adjacency Matrix — 4× in this paper · explained in 1 episode
• Nearest Neighbours — 4× in this paper
• Spectral clustering — 4× in this paper · explained in 2 episodes
• Bayesian Optimization — 2× in this paper · explained in 3 episodes
• Curse of Dimensionality — 2× in this paper · explained in 6 episodes
• Uniform Manifold Approximation and Projection — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Adjusted Rand Index — 1× in this paper
• Benchmark — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• A Survey of Dimension Estimation Methods
• Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification
• Locally Linear Embedding and its Variants: Tutorial and Survey
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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