Non-Metric Space Library Manual

2015/08/22 by Bilegsaikhan Naidan, Leonid Boytsov, Naidan, Bilegsaikhan +5 · 2 citations
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Data Management and Algorithms #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Mathematical Software (cs.MS) #Robotics and Sensor-Based Localization #cs.IR #cs.MS

paper · pdf · doi:10.48550/arxiv.1508.05470

Methodology paper

openalex publication_date 2015/08/22 · arxiv created 2019/06/07 · arxiv updated 2019/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This document covers a library for fast similarity (k-NN)search. It describes only search methods and distances (spaces). Details about building, installing, Python bindings can be found online:https://github.com/searchivarius/nmslib/tree/v1.8/. Even though the library contains a variety of exact metric-space access methods, our main focus is on more generic and approximate search methods, in particular, on methods for non-metric spaces. NMSLIB is possibly the first library with a principled support for non-metric space searching.

Cited by

• SIEVE: Effective Filtered Vector Search with Collection of Indexes
• Production Ranking Systems: A Review

Related

• The SuperM-Tree: Indexing metric spaces with sized objects
• Exact Trajectory Similarity Search With N-tree: An Efficient Metric Index for kNN and Range Queries
• Ball*-tree: Efficient spatial indexing for constrained nearest-neighbor search in metric spaces
• Let them have CAKES: A Cutting-Edge Algorithm for Scalable, Efficient, and Exact Search on Big Data
• High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations