ATLAS: A Probabilistic Algorithm for High Dimensional Similarity Search
Summary: ATLAS: probabilistic high-dimensional similarity search for binary vectors. Employs truly random permutations to filter candidates and estimate similarity, achieving 97.5% recall and up to 100x speedups over exact/approx methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiaqi Zhai
- 2. Yin Lou
- 3. Johannes Gehrke
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 78 | A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces | 1998 | VLDB | 0.00056385781 |
| 248 | Efficient set joins on similarity predicates | 2004 | SIGMOD | 0.00030888982 |
| 264 | Efficient Exact Set-Similarity Joins | 2006 | VLDB | 0.00029950264 |
| 340 | CURE: An Efficient Clustering Algorithm for Large Databases | 1998 | SIGMOD | 0.00026854084 |
| 442 | Efficient Parallel Set-Similarity Joins Using MapReduce | 2010 | SIGMOD | 0.00023095823 |
| 4,096 | Finding Near Neighbors Through Cluster Pruning | 2007 | PODS | 6.4506806e-05 |
| 6,316 | On the Effects of Dimensionality Reduction on High Dimensional Similarity Search | 2001 | PODS | 5.1098143e-05 |
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