Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
-
Updated
Feb 6, 2024 - C++
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Query-Aware LSH for Approximate NNS (PVLDB 2015 and VLDBJ 2017)
Fast and precise comparison of genomes and metagenomes (in the order of terabytes) on a typical personal laptop
BagMinHash - Minwise Hashing Algorithm for Weighted Sets
ProbMinHash – A Class of Locality-Sensitive Hash Algorithms for the (Probability) Jaccard Similarity
SetSketch: Filling the Gap between MinHash and HyperLogLog
Software for exploration of gene expression data from single-cell RNA sequencing.
A fast high dimensional near neighbor search algorithm based on group testing and locality sensitive hashing
TreeMinHash: Fast Sketching for Weighted Jaccard Similarity Estimation
C++ program that, given a vectorised dataset and query set, performs locality sensitive hashing, finding either Nearest Neighbour (NN) or Neighbours in specified range of points in query set, using either Euclidian distance or Cosine Similarity.
Generate kmers/minimizers/hashes/MinHash signatures, including with multiple kmer sizes.
Point-to-Hyperplane NNS Beyond the Unit Hypersphere (SIGMOD 2021)
ANN - Approximate Nearest Neighbors Index with Locality Sensitive Hashing and Hyper Cube projections for vectors and multi-dimensional data.
C++ implementation of Locality-Sensitive Hashing over txt documents, using Jaccard Similarity.
Query-Aware LSH for Approximate NNS (In-Memory Version of QALSH)
Nearest neighbor search. Methods: LSH, hypercube, and exhaustive search. C++
similarity search and clustering algorithms for time-series represented as euclidean polygonal curves
Long Reads Mapping Algorithms
HSEARCH: fast and accurate protein sequence motif search and clustering
approximation algorithms for exact nearest neighbors search and clustering on multi-dimensional vectors
Add a description, image, and links to the locality-sensitive-hashing topic page so that developers can more easily learn about it.
To associate your repository with the locality-sensitive-hashing topic, visit your repo's landing page and select "manage topics."