Papers › A scalable solution to the nearest neighbor search problem through local-search...
A scalable solution to the nearest neighbor search problem through local-search methods on neighbor graphs
Eric S. Tellez, Guillermo Ruiz, Edgar Chavez, Mario Graff
Near neighbor search (NNS) is a powerful abstraction for data access; however, data indexing is troublesome even for approximate indexes. For intrinsically high-dimensional data, high-quality fast searches demand either indexes with impractically large memory usage or preprocessing time. In this paper, we introduce an algorithm to solve a nearest-neighbor query q by minimizing a kernel function defined by the distance from q to each object in the database. The minimization is performed using metaheuristics to solve the problem rapidly; even when some methods in the literature use this strategy behind the scenes, our approach is the first one using it explicitly. We also provide two approaches to select edges in the graph's construction stage that limit memory footprint and reduce the number of free parameters simultaneously. We carry out a thorough experimental comparison with state-of-the-art indexes through synthetic and real-world datasets; we found out that our contributions achieve competitive performances regarding speed, accuracy, and memory in almost any of our benchmarks.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections