Research map / Learning theory and algorithms / Prediction and decision losses
C-MinHash: Improving Minwise Hashing with Circulant Permutation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace K independent permutations with two permutations: initial random σ plus circulant shifts of π | MinHash · theory and synthetic data (D=128) | estimation variance / MSE | Helped |
| apply initial random permutation σ before circulant hashing (C-MinHash-(σ,π)) instead of none (C-MinHash-(0,π)) (ablation) | C-MinHash · BBC, NIPS, MNIST, CIFAR | MAE of Jaccard estimation | Helped |
| use only one permutation π for both initial shuffle and circulant hashing (C-MinHash-(π,π)) (ablation) | C-MinHash-(σ,π) · Words dataset and BBC/NIPS/MNIST/CIFAR | MSE and MAE | No clear effect |
| circulant shifting without initial permutation | MinHash-style circulant hashing · synthetic structured data | variance relative to MinHash | Mixed |
| increase K (number of hashes) for C-MinHash-(σ,π) | C-MinHash-(σ,π) variance ratio over MinHash · theory, f/D sweep | variance ratio MinHash/C-MinHash | Helped |
Closest papers by meaning
- Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling (NeurIPS 2019 · similarity 0.91)
- Simple and Efficient Weighted Minwise Hashing (NeurIPS 2016 · similarity 0.89)
- Practical Hash Functions for Similarity Estimation and Dimensionality Reduction (NeurIPS 2017 · similarity 0.88)
- Optimal Densification for Fast and Accurate Minwise Hashing (ICML 2017 · similarity 0.88)
- S2JSD-LSH: A Locality-Sensitive Hashing Schema for Probability Distributions (AAAI 2017 · similarity 0.87)
- Statistical Model-driven Similarity Hashing: Bridging Modalities for Efficient Unsupervised Retrieval (AAAI 2025 · similarity 0.84)
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