Robust Hashing With Local Models for Approximate Similarity Search
Robust Hashing With Local Models for Approximate Similarity Search is a scholarly work by Xuelong Li and Zi Huang, published in 2014 in ''IEEE Transactions on Cybernetics''. The main subjects of the publication include hash function, hash table, rolling hash, Linear hashing, data mining, K-independent hashing, feature hashing, theoretical computer science, double hashing, nearest neighbor search, Multiple object tracking, dynamic perfect hashing, universal hashing, computer science, feature engineering, Locality-sensitive hashing, and algorithm. The authors propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional data points into binary hash codes by effectively utilizing local structural information.