We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance
that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal …

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 …

Data similarity (or distance) computation is a fundamental research topic which underpins
many high-level applications based on similarity measures in machine learning and data …

In recent years, both hashing-based similarity search and multimodal similarity search have
aroused much research interest in the data mining and other communities. While hashing …

Hashing techniques have been intensively investigated in the design of highly efficient
search engines for largescale computer vision applications. Compared with prior …

Locality-sensitive hashing (LSH) is a basic primitive in several large-scale data processing
applications, including nearest-neighbor search, de-duplication, clustering, etc. In this paper …

Rank correlation measures are known for their resilience to perturbations in numeric values
and are widely used in many evaluation metrics. Such ordinal measures have rarely been …

Scalable similarity search is the core of many large scale learning or data mining
applications. Recently, many research results demonstrate that one promising approach is …

Background Single cell transcriptomics is critical for understanding cellular heterogeneity
and identification of novel cell types. Leveraging the recent advances in single cell RNA …

Kernel methods have been shown to be effective for many machine learning tasks such as
classification and regression. In particular, support vector machines with the Gaussian …