[arXiv] BigDataFr recommends: Learning to Hash for Indexing Big Data – A Survey

BigDataFr recommends: Learning to Hash for Indexing Big Data – A Survey

The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution using exhaustive comparison is infeasible due to the prohibitive computational complexity and memory requirement.

In response, Approximate Nearest Neighbor (ANN) search based on hashing techniques has become popular due to its promising performance in both efficiency and accuracy. Prior randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore data-independent hash functions with random projections or permutations.  ‘ […]

Read paper
By Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang
Source: arxiv.org

Laisser un commentaire

Votre adresse de messagerie ne sera pas publiée. Les champs obligatoires sont indiqués avec *