Analysing Indexability of Intrinsically High-Dimensional Data Using TriGen

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10420915" target="_blank" >RIV/00216208:11320/20:10420915 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-60936-8_20" target="_blank" >https://doi.org/10.1007/978-3-030-60936-8_20</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-60936-8_20" target="_blank" >10.1007/978-3-030-60936-8_20</a>

Result language
angličtina
Original language name
Analysing Indexability of Intrinsically High-Dimensional Data Using TriGen
Original language description
The TriGen algorithm is a general approach to transform distance spaces in order to provide both exact and approximate similarity search in metric and non-metric spaces. This paper focuses on the reduction of intrinsic dimensionality using TriGen. Besides the well-known intrinsic dimensionality based on distance distribution, we inspect properties of triangles used in metric indexing (the triangularity) as well as properties of quadrilaterals used in ptolemaic indexing (the ptolemaicity). We also show how LAESA with triangle and ptolemaic filtering behaves on several datasets with respect to the proposed indicators.
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/GA19-01641S" target="_blank" >GA19-01641S: Contextual Similarity Search in Open Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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