Research map / Learning theory and algorithms / Prediction and decision losses
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use mixed tabulation instead of multiply-shift for OPH | OPH similarity estimation · Synthetic data | similarity-estimation mean squared error and concentration | Helped |
| Use mixed tabulation instead of multiply-shift or 2-wise PolyHash for feature hashing | Feature hashing · Synthetic data | squared L2 norm concentration | Helped |
| Use mixed tabulation instead of weaker hash functions | Feature hashing · MNIST and News20 | squared L2 norm concentration: 16.671 vs 2.077 | Helped |
| Use mixed tabulation instead of multiply-shift | OPH-based LSH · MNIST and News20, K=L=10 | retrieved / recall-ratio | Helped |
| Use multiply-shift instead of mixed tabulation | OPH-based LSH · MNIST and News20, K=L=10 | recall | Helped |
| Use mixed tabulation instead of MurmurHash3 | Hash evaluation · 10^7 random integers and News20 | hashing time: 42.98{\,ms} vs 59.70{\,ms}; 90.55{\,ms} vs 159.44{\,ms} | Helped |
| Use mixed tabulation instead of multiply-shift | Hash evaluation · 10^7 random integers | hashing time: 42.98{\,ms} vs 7.72{\,ms} | Hurt |
Closest papers by meaning
- Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling (NeurIPS 2019 · similarity 0.88)
- C-MinHash: Improving Minwise Hashing with Circulant Permutation (ICML 2022 · similarity 0.88)
- Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond (NeurIPS 2019 · similarity 0.87)
- An ensemble diversity approach to supervised binary hashing (NeurIPS 2016 · similarity 0.87)
- Statistical Model-driven Similarity Hashing: Bridging Modalities for Efficient Unsupervised Retrieval (AAAI 2025 · similarity 0.85)
- Effective Comparative Prototype Hashing for Unsupervised Domain Adaptation (AAAI 2024 · similarity 0.85)
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