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  • The recent techniques for approximate similarity search focus on optimizing answer precision/recall and they typically improve the average of these measures over a set of sample queries. However, according to our observation, the recall for particular indexes and queries can fluctuate considerably. In order to stabilize the recall, we propose a query-evaluation model that exploits several variants of the search index. This approach is applicable to a signicant subset of current approximate methods with a focus on techniques based purely on metric postulates. Applying this approach to the M-Index structure, we perform extensive measurements on large datasets and we show that this approach has a positive impact on the recall stability and it suppresses the most unsatisfactory cases. Further, the results indicate that the proposed approach can also increase the general average recall for given overall search costs.
  • The recent techniques for approximate similarity search focus on optimizing answer precision/recall and they typically improve the average of these measures over a set of sample queries. However, according to our observation, the recall for particular indexes and queries can fluctuate considerably. In order to stabilize the recall, we propose a query-evaluation model that exploits several variants of the search index. This approach is applicable to a signicant subset of current approximate methods with a focus on techniques based purely on metric postulates. Applying this approach to the M-Index structure, we perform extensive measurements on large datasets and we show that this approach has a positive impact on the recall stability and it suppresses the most unsatisfactory cases. Further, the results indicate that the proposed approach can also increase the general average recall for given overall search costs. (en)
Title
  • Stabilizing the Recall in Similarity Search
  • Stabilizing the Recall in Similarity Search (en)
skos:prefLabel
  • Stabilizing the Recall in Similarity Search
  • Stabilizing the Recall in Similarity Search (en)
skos:notation
  • RIV/00216224:14330/11:00073202!RIV15-MV0-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA201/09/0683), P(GAP103/10/0886), P(GPP202/10/P220), P(VF20102014004), 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
  • 231881
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/11:00073202
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • locality-sensitive hashing; metric space; similarity search; recall; stability (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [C5AE13BE0C14]
http://linked.open...v/mistoKonaniAkce
  • Lipary, Italy
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • Fourth International Conference on Similarity Search and Applications, SISAP 2011
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://bibframe.org/vocab/doi
  • 10.1145/1995412.1995422
http://purl.org/ne...btex#hasPublisher
  • ACM Press
https://schema.org/isbn
  • 9781450307956
http://localhost/t...ganizacniJednotka
  • 14330
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