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@inproceedings{899282,
author = {Novák, David and Kyselák, Martin and Zezula, Pavel},
address = {New York},
booktitle = {3rd International Conference on Similarity Search and Applications},
keywords = {locality-sensitive hashing; metric space; similarity search; approximation; scalability},
howpublished = {tištěná verze "print"},
language = {eng},
location = {New York},
isbn = {978-1-4503-0420-7},
pages = {59-66},
publisher = {ACM Press},
title = {On Locality-sensitive Indexing in Generic Metric Spaces},
year = {2010}
}
TY - JOUR
ID - 899282
AU - Novák, David - Kyselák, Martin - Zezula, Pavel
PY - 2010
TI - On Locality-sensitive Indexing in Generic Metric Spaces
PB - ACM Press
CY - New York
SN - 9781450304207
KW - locality-sensitive hashing
KW - metric space
KW - similarity search
KW - approximation
KW - scalability
N2 - The concept of Locality-sensitive Hashing (LSH) has been successfully used for searching in high-dimensional data and a number of locality-preserving hash functions have been introduced. In order to extend the applicability of the LSH approach to a general metric space, we focus on a recently presented Metric Index (M-Index), we redefine its hashing and searching process in the terms of LSH, and perform extensive measurements on two datasets to verify that the M-Index fulfills the conditions of the LSH concept. We widely discuss "optimal" properties of LSH functions and the efficiency of a given LSH function with respect to kNN queries. The results also indicate that the M-Index hashing and searching is more efficient than the tested standard LSH approach for Euclidean distance.
ER -
NOVÁK, David, Martin KYSELÁK and Pavel ZEZULA. On Locality-sensitive Indexing in Generic Metric Spaces. In \textit{3rd International Conference on Similarity Search and Applications}. New York: ACM Press, 2010, p.~59-66. ISBN~978-1-4503-0420-7.
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