Cardinality Estimation for High Dimensional Similarity Queries with Adaptive Bucket Probing
📰 ArXiv cs.AI
A framework for cardinality estimation in high-dimensional similarity search using adaptive bucket probing with locality-sensitive hashing
Action Steps
- Leverage locality-sensitive hashing (LSH) to partition the vector space
- Apply the principles of classical multi-probe LSH to preserve distance proximity
- Implement adaptive bucket probing to improve online efficiency and estimate cardinality accurately
Who Needs to Know This
Data scientists and AI engineers working on similarity search and high-dimensional data analysis can benefit from this research to improve the efficiency and accuracy of their models
Key Insight
💡 Locality-sensitive hashing can be used to design a lightweight and efficient framework for cardinality estimation in high-dimensional spaces
Share This
📊 Improve similarity search with adaptive bucket probing and LSH!
DeepCamp AI