A Projection-based Locality-Sensitive Hashing Technique for Reducing False Negatives

It is challenging to efficiently find similar pairs of objects when the number of objects is huge. The locality-sensitive hashing techniques have been developed to address this issue. They employ the hash functions to map objects into buckets, where similar objects have high chances to fall into the same buckets. This paper is concerned with a locality-sensitive hashing technique, the projection-based method, which is applicable to the Euclidean distance-based similar pair identification problem. It proposes an extended method which allows an object to be hashed to more than one bucket by introducing additional hashing functions. From the experimental studies, it has been shown that the proposed method could provide better performance compared to the projection-based method.