Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many
diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular …

Recently, there has been a pressing need to manage high-dimensional vector data in data
science and AI applications. This trend is fueled by the proliferation of unstructured data and …

Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential
and fundamental operation in many applications from many domains such as multimedia …

With the development of learning-based embedding models, embedding vectors are widely
used for analyzing and searching unstructured data. As vector collections exceed billion …

Locality sensitive hashing (LSH) is a widely practiced c-approximate nearest neighbor (c-
ANN) search algorithm in high dimensional spaces. The state-of-the-art LSH based …

Locality-sensitive hashing (LSH) is a promising family of methods for the high-dimensional
approximate nearest neighbor (ANN) search problem due to its sub-linear query time and …

Vector quantization is one of the most popular techniques for approximate nearest neighbor
(ANN) search. Over the past decade, many vector quantization methods have been …

Learning to hash is of particular interest in information retrieval for large-scale data due to its
high efficiency and effectiveness. Most studies in hashing concentrate on constructing new …

Spatial relationships are core components in the design and definition of spatial queries. A
spatial relationship determines how two or more spatial objects are related or connected in …

Locality-sensitive hashing (LSH) has gained ever-increasing popularity in similarity search
for large-scale data. It has competitive search performance when the number of generated …