Locality Sensitive Hashing

Locality-sensitive hashing (LSH) is a technique for efficiently finding approximate nearest neighbors in high-dimensional data, addressing the computational challenges of exhaustive searches. Current research focuses on improving LSH's speed and accuracy through novel hash function designs, integration with neural networks (e.g., learned LSH, Siamese networks), and applications to various data types including tensors and sets. This work is significant because efficient nearest neighbor search is crucial for numerous applications, ranging from data deduplication and anomaly detection to improving the performance of machine learning algorithms like symbolic regression and federated learning.