Permutation Hashing

Permutation hashing is a technique for efficiently generating compact binary representations (hash codes) of data, primarily used for approximate nearest neighbor search and similarity estimation in large datasets. Current research focuses on improving the accuracy and efficiency of permutation hashing algorithms, including variations like one permutation hashing (OPH) and its refinements, and integrating it with deep learning models for tasks such as image retrieval and cross-modal hashing. These advancements are significant because they enable faster and more scalable similarity search in applications ranging from information retrieval to machine learning, particularly where data dimensionality poses challenges.