Non-metric similarity search using genetic TriGen

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10407733" target="_blank" >RIV/00216208:11320/19:10407733 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21240/19:00335054

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-030-32047-8_8" target="_blank" >http://dx.doi.org/10.1007/978-3-030-32047-8_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-32047-8_8" target="_blank" >10.1007/978-3-030-32047-8_8</a>

Result language

angličtina

Original language name

Non-metric similarity search using genetic TriGen

Original language description

The metric space model is a popular and extensible model for indexing data for fast similarity search. However, there is often need for broader concepts of similarities (beyond the metric space model) while these cannot directly benefit from metric indexing. This paper focuses on approximate search in semi-metric spaces using a genetic variant of the TriGen algorithm. The original TriGen algorithm generates metric modifications of semi-metric distance functions, thus allowing metric indexes to index non-metric models. However, &quot;analytic&quot; modifications provided by TriGen are not stable in predicting the retrieval error. In our approach, the genetic variant of TriGen - the TriGenGA - uses genetically learned semi-metric modifiers (piecewise linear functions) that lead to better estimates of the retrieval error. Additionally, the TriGenGA modifiers result in better overall performance than original TriGen modifiers.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA17-22224S" target="_blank" >GA17-22224S: User preference analytics in multimedia exploration models</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Computer Science

ISBN

978-3-030-32046-1

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

86-93

Publisher name

Springer International Publishing

Place of publication

Cham

Event location

Newark NJ, USA

Event date

Oct 2, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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