Index structures for fast similarity search of binary vectors / Rachkovskij. (2017)
Ukrainian

English  Cybernetics and Systems Analysis   /     Issue (2017, 53 (5))

Rachkovskij D.A.
Index structures for fast similarity search of binary vectors

This article reviews index structures for fast similarity search for objects represented by binary vectors (with components equal to 0 or 1). Structures for both exact and approximate search by Hamming distance and other similarity measures are considered. Mainly., index structures are presented that are based on hash tables and similarity-preserving hashing and also on tree structures, neighborhood graphs, and distributed neural autoassociative memory. Ideas of well-known algorithms and algorithms proposed in recent years are stated. © 2017, Springer Science+Business Media, LLC.

Keywords: Hamming distance, index structure, locality-sensitive hashing, multi-index hashing, near neighbor, nearest neighbor, neighborhood graph, neural autoassociative memory, similarity search, tree structure, Bins, Forestry, Hamming distance, Nearest neighbor search, Auto-associative Memory, Index structure, Locality sensitive hashing, Multi-index, near neighbor, Nearest neighbors, Neighborhood graphs, Similarity search, Tree structures, Trees (mathematics)


Cite:
Rachkovskij D.A. (2017). Index structures for fast similarity search of binary vectors. Cybernetics and Systems Analysis, 53 (5), 167-192. doi: https://doi.org/10.1007/s10559-017-9983-x http://jnas.nbuv.gov.ua/article/UJRN-0000754495 [In Russian].


 

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