About: On Locality-sensitive Indexing in Generic Metric Spaces     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • 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 %22optimal%22 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.
  • 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 %22optimal%22 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. (en)
Title
  • On Locality-sensitive Indexing in Generic Metric Spaces
  • On Locality-sensitive Indexing in Generic Metric Spaces (en)
skos:prefLabel
  • On Locality-sensitive Indexing in Generic Metric Spaces
  • On Locality-sensitive Indexing in Generic Metric Spaces (en)
skos:notation
  • RIV/00216224:14330/10:00044857!RIV11-GA0-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0545), P(GA201/09/0683), P(GAP103/10/0886), P(GPP202/10/P220), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 276741
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/10:00044857
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • locality-sensitive hashing; metric space; similarity search; approximation; scalability (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [ACF33C77646B]
http://linked.open...v/mistoKonaniAkce
  • Istanbul, Turkey
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • 3rd International Conference on Similarity Search and Applications
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Novák, David
  • Zezula, Pavel
  • Kyselák, Martin
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • ACM Press
https://schema.org/isbn
  • 978-1-4503-0420-7
http://localhost/t...ganizacniJednotka
  • 14330
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 77 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software