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 …

Binary code fingerprinting is crucial in many security applications. Examples include
malware detection, software infringement, vulnerability analysis, and digital forensics. It is …

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose
distances to a query item are the smallest from a large database. Various methods have …

Approximate nearest neighbor search (ANNS) is a fundamental problem in databases and
data mining. A scalable ANNS algorithm should be both memory-efficient and fast. Some …

Locality-Sensitive Hashing (LSH) and its variants are the well-known indexing schemes for
the c-Approximate Nearest Neighbor (c-ANN) search problem in high-dimensional …

Data series are a special type of multidimensional data present in numerous domains,
where similarity search is a key operation that has been extensively studied in the data …

Compact hash codes can facilitate large-scale multimedia retrieval, significantly reducing
storage and computation. Most hashing methods learn hash functions based on the data …

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 …

Zero-shot hashing targets to learn the hash codes of images in unseen classes based on the
limited training data provided by seen classes. In zero-shot hashing, transferring the …

Nearest neighbor searching of large databases in high-dimensional spaces is inherently
difficult due to the curse of dimensionality. A flavor of approximation is, therefore, necessary …